Expression of Interest (EOI) Development and Implementation of a Project-based AI Training Program for Rwandan Public Institutions

Expression of Interest (EoI)

Development and implementation of a project-based AI training program for Rwandan public institutions

Reference Number: 83483852

Date of Publication: 03.03.2025

0. Context

The Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH is a federally owned international cooperation enterprise for sustainable development with worldwide operations. The GIZ Office in Kigali covers GIZ’s portfolio in Rwanda and Burundi. GIZ Rwanda/Burundi implements projects on behalf of the German Federal Ministry for Economic Cooperation and Development, the European Union and other commissioning authorities in the following priority areas: Sustainable Economic Development, Good Governance, Climate, Energy and Sustainable Urban Development, Digitalization and Digital Economy, Mineral Governance, Peace and Security in the Great Lakes Region.

About the GIZ Digital Transformation Center Rwanda

The Digital Transformation and Digital Economy Cluster of GIZ Rwanda is a Rwandan German initiative to develop impact-driven digital solutions for Rwanda and beyond. Therefore, it provides not only advisory services and training for government institutions and local tech companies but also a modern space to boost creativity and collaboration: The Digital Transformation Center

About the Digital Transformation Center’s AI Hub

One of the focus areas of the Cluster Digital Transformation & Digital Economy is Artificial Intelligence. Through the AI Hub Rwanda at the Digital Transformation Center, GIZ contributes to sustainable and open approaches to AI in Rwanda and at the regional level. This includes skills and capacity building, creating and providing AI training data openly on accessible platforms, and developing policy frameworks to promote AI adoption in Rwanda. A specific objective is to make AI solutions available for the Rwandan population in Kinyarwanda.

Therefore, the Digital Transformation Center and particularly the global GIZ initiative FAIR Forward contribute to creating AI training data sets and piloting AI-based solutions in the areas of natural language processing (NLP, e.g.: voice assistants) and machine translation.

The vision of the AI Hub Rwanda is to co-create a vibrant and inclusive ecosystem in Rwanda harnessing the benefits of open and ethical AI for sustainable development. The mission is empowering our partners through providing open AI training data, capacity building, and development of ethical policy frameworks towards building community-driven AI solutions.

Specifically, the AI Hub has 3 key areas of intervention:

Capacity building: We aim to strengthen the capacities of local actors (local developers, universities, companies, governments, and civil society) in the development and application of artificial intelligence. For this purpose, we offer "on-the-job" training as well as specific training formats.
AI Policy:We advocate for value-based AI that is rooted in human rights, international norms such as accountability, transparency of decision-making and privacy, and draws on European experiences such as the EU General Data Protection Regulation (GDPR). Therefore, the project supports the development of effective political and regulatory frameworks.
Open data, models and use cases for Natural Language Processing (NLP) and Machine Learning for Earth Observation (ML4EO):We facilitate the provision of open, non-discriminatory and inclusive training data and open-source AI applications. Open access to African language data is a key priority to enable the development of AI-based voice interaction in local languages to empower marginalized groups.

Training Program for AI professionals in the public sector

Contextual Background

Rwanda has made significant progress in leveraging AI for sustainable socio-economic development and inclusive growth. Among the initiatives implemented involves the development of Rwanda's National Artificial Intelligence Policy, a collaborative effort led by MINICT and RURA, supported by GIZ FAIR Forward, the Centre for the 4th Industrial Revolution Rwanda (C4IR), and The Future Society (TFS). This policy serves as a strategic framework aimed at maximizing the benefits of AI while addressing associated risks. One of the key focus areas outlined in the policy is equipping the workforce with 21st-century AI and data skills to accelerate responsible AI development and deployment across the country.

Despite these ambitions, government and public sector entities have encountered challenges in adopting AI technologies at the pace seen in the private sector. Higher salaries offered by private companies to AI professionals pose a significant barrier to smaller-budget public institutions, impeding their ability to integrate innovative technologies effectively. Additionally, bureaucratic processes often slow down the adoption of AI solutions in public services, which are essential for enhancing operational efficiency and enabling more informed decision-making. For effective regulation of digital industries without hindering innovation, government and public sector workers need a solid understanding of the underlying technology. A values-based AI ecosystem would not be possible if actors neither understand the technologies, the terminologies or the environment.

To address these challenges and support the fulfilment of the objectives outlined in the National AI Policy, GIZ intends to implement a specialized training programme for AI professionals working in Rwandan public institutions with a focus on advanced data science and artificial intelligence. By equipping public sector employees with advanced AI competencies, this training program aims to empower them to leverage AI technologies effectively in their respective roles. The contents of the training shall be uploaded on an e-learning platform to be made accessible to Rwanda innovation ecosystem.

Target Group

The training program should target the following key user groups:

20 AI professionals in public institutions, including developers, business analysts, data scientists, data engineers, and ML engineers, seeking to upskill, reskill, or acquire applied knowledge in AI.
Applicants to the program should have educational or professional background in fields related to data science, machine learning, computer science, software engineering, and information technology. Additionally, participants should also have experience with at least one of the following programming languages: Python, R or structured query language (SQL)

Main Objectives

Strengthen the skills and knowledge of AI professionals and business analysts in the fundamentals of AI principles, Machine Learning (ML), Natural Language Processing (NLP), and Deep Learning, while also enhancing business analysts' ability to apply AI insights to business strategy, data analysis, and decision-making processes, guiding them in tailoring these skills to meet their institutional requirements.
Curate a curriculum of instructional content free of charge and designed as Open Educational Resources (OER), enabling other interested developers to access the educational material at no expense.

Format and Structure

This training will adopt a project-based approach, where each participating institution will submit an AI project proposal aligned with its objectives. Participants will work on these projects, allowing them to directly apply AI skills and knowledge to real-world scenarios that are relevant to their institution’s goals.
The AI-driven solutions to be developed through this training will encompass a range of diverse AI domains, including predictive modeling with dashboard integration, computer vision (CV), natural language processing (NLP), and other machine learning solutions such as Machine Learning for Earth Observation (ML4EO), AI for business analysis, etc.

Designing the training structure for a diverse group with varying levels of AI integration necessitates a tailored and adaptable approach. The contractor will organize the training to address different stages of AI adoption, ensuring all participants benefit regardless of their current skill levels. In terms of content, the training will be divided into two segments: core AI content modules and practical project implementation.

Core content AI modules: will deliver foundational knowledge required for project initiation, covering essential concepts in machine learning and deep learning, as well as key AI tools and platforms. Participants will engage in model training, validation, and deployment, followed by modules tailored to their specific project needs. Introductory content will be provided based on the project focus, such as NLP, CV, etc. Participants working on NLP solutions, like chatbots, will not receive introductory modules on CV, but rather will follow tracks relevant to their particular domain. This part of the training will have a maximum duration 1 month.
Practical projects implementation: Participants will have the opportunity to apply the AI skills to real-world projects within their institutions, under the guidance of experienced mentors to develop a real-world working prototype that could be scaled for further development. This part of the training will last for 3 months.
The course will extend over 5 months and will be organized into two groups. The initial Core content AI modulesin-class training session will focus on the Natural Language Processing (NLP) domain, with the first group completing this phase and becoming ready to work on their projects. Upon completion (after the first month of the training), the second cohort, focusing on Machine Learning (ML) and Computer Vision (CV), will begin their training.
The learning format will integrate both online and in-person components. The course will feature online learning for the theoretical aspects and offline (in-person) elements such as exercises, practical sessions and on-site technical support as well as additional online elements. The participants will be hosted at a single venue throughout the program.

Tasks to be performed by the contractor

The contractor will be responsible for the development, implementation and follow-up of the training program. In details, the contractor is responsible for providing the following services:

Work package 1: Needs Assessment and Skill Gap Analysis for Project implementation

The needs assessment phase will focus on identifying the specific requirements each participant needs to successfully implement their institution projects. It aims to determine the skills, data or resources necessary for participants to effectively deliver their project's solution. This involves:

Conduct initial meetings with each participant to evaluate their current technical skills and identify any gaps relative to the skills required for their project. This will help assess each participant's unique challenges and strengths in implementing their project.
Analyse collected data to identify common themes and specific needs of each institution project.
Collaborate with each participant to set clear project goals, define the problem to be solved, and outline the solution. This includes specifying objectives such as developing a predictive model or creating a chatbot, and establishing success criteria like model performance, response accuracy, business impact, etc.

Deliverables

Needs assessment report outlining key findings, including skill gaps, challenges, and training priorities for each participant.
Prepare a detailed report on the project implementation, covering required skillsets, datasets, resources, and the problem to be addressed. Provide recommendations for the institution on how to effectively carry out the project.
Program structure document detailing the curriculum framework, learning objectives, and delivery schedule.

Work package 2: Design the course format and curriculum for the training Program.

Develop concept for training implementation, including roles and responsibilities of contractor and local sub-contractor for teaching, logistics, etc
Design course materials, create hands-on exercises, case studies, and projects to reinforce learning and practical application of skills.
Incorporate feedback from stakeholders and subject matter experts to refine the curriculum content.
In the curriculum, describe the course design including
instruction methods (individual or group instruction, self-study, lectures, practical coding sessions, etc.) appropriate for the content,
timeline (training days including times spend in lectures, on exercise, in group work and/or other activities),
Finalize the curriculum and prepare it for implementation.
Based on the information gathered, design the curriculum that satisfies the needs of each participant and provides the knowledge and skills required to enhance their AI skills. All content should focus on the Rwandan context, applying in the curriculum resources previously developed through the FAIR Forward projects.
Collaborate with potential academic partners to leverage their expertise and insights during the curriculum development process. This includes involving academic experts in co-designing the curriculum to ensure it is robust and aligned with current academic standards.
Prepare and provide technical hybrid (online/remote) training in Kigali, Rwanda for the participants based on the curriculum in cooperation with FF. In their bid, the contractor must clearly outline how they will enhance the interactivity of the training program by utilizing advanced interactive platforms and AI simulation tools. This includes specifying the use of platforms such as Google AI, Repl.it, CodeSandbox, Codeshare.io, or other similar platforms and learning management systems like Canvas or Moodle, rather than relying solely on Zoom or Google Meet, which have proven ineffective.The contractor should provide a detailed strategic plan addressing this approach and describe how they will monitor participant progress in a hybrid training setting.

Deliverables

Training implementation document
Comprehensive curriculum design
Curriculum package
Training evaluation plan

Work package 3: Selection & Training Delivery

Selecting the developers and Senior developers from the public institutions

The selection of participants will be finalized prior to the commencement of the contract. Upon initiation of the contract, the contractor will be informed of the specific participants and institutions with which they will be collaborating. This process will be overseen through coordinated efforts among the MINICT, AI Hub, and RISA.

MINICT, in collaboration with the AI hub team, will design an application form for Chief Data Officers (CDOs) to submit the use cases they wish to implement within their institution and to nominate a participant to work on the project.
Design a trainee profile and provide clear expectations for the participants including length of the instructional training.
Create and implement an outreach campaign in close collaboration with AI hub, including creating an application form and advertising on social media, other suitable media and in partner universities to attract other suitable participants.
Create an evaluation scheme that features inclusive selection criteria that ensure that the programme does not unduly exclude under-represented groups.
Coordinate with RISA & MINICT to select 20 participants, ideally of which at least 40% are women.
Conduct pre-program survey (based on GIZ template): Design and implement a pre-program survey that assesses knowledge, motivation, expectation, and interests for the practical phase of the participants. Results shall be used to further define the format and content of the curriculum.

Implementing the training programme

Conduct interactive opening and closing meetings with each institution CDOs and the assigned participants from the training program to agree on the format of exchange and priority use cases.

Provide training materials to the participants based on the approved curriculum. The training material includes:

Course objectives, outlines, and syllabi
Presentations slides and scripts of the training content
Solutions to exercises including solution code
Multimedia material including videos, recordings of webinars and training sessions.
Access to the training datasets for model development. FF can assist by providing relevant training datasets and models, particularly for NLP and ML4EO, if these topics are included in the training.
The contractor shall provide the technical knowledge and conduct in-person and remote teaching, providing in-class tutoring assistance during exercises and other practical sessions, venue, appropriate set-up for remote teaching, and logistics during this training.
Conduct regular evaluations (at least three) both on the learning progress and satisfaction of the participants with the training and optimize accordingly (as well as report on the evaluation and optimization to FAIR Forward). The feedback shall be used to adapt the curriculum where necessary.
Manage communication platforms (e.g., based on Slack, WhatsApp, etc.) for participants to facilitate the exchange of relevant news and updates. The channel will also be used as a networking platform after the training.
Manage all communication with the participants and the general public in close coordination with the AI hub Rwanda team

Deliverables

Training schedule and session materials
Feedback surveys and attendance records
Progress reports
Open Educational Resources published on GitHub and handed over to MINICT for future training and sustainability purposes.

Work package 4: Coordination, Development of POC’s & Mentorship and Coaching

The contractor will develop and implement a project-based training module which will provide the participants with knowledge and skills to accompany the work on their proof of concept (POCs) development with a strong focus on providing technical experience to the data teams in the selected public sector institutions.

Develop and implement an additional physical"Design thinking, Business development and Ethical AI module". The module should include, but not be limited to:

User-centred design for designing solutions which meet user needs.
Developing a business model for an AI project: creating a viable business model for AI projects, ensuring they are financially and operationally sustainable.
Public sector innovation and AI: exploring how AI can be leveraged to drive innovation within the public sector and improve government services
Addressing ethical considerations and challenges related to AI. It should include both global trends and Rwandan specifics to align with international standards and local regulations.
Provide technical mentoring and coaching to the participants during the project implementation phase. The goal is to support the teams to create proof of concept within the given timeframe.

Coordinate and manage communication with key government partners such as RISA, CDO office and MINICT. The communication should include but not be limited to:

Progress reports
Technical documentation of POCs
Institutional AI analysis reference documents

Pair participants with experienced local senior experts who can provide individualized guidance and support.
Facilitate regular mentoring sessions to review progress, address challenges, and set goals for skill development.
Monitor mentorship relationships and provide additional support or resources as needed to maximize impact.
Organise and implement multi-stakeholder meetings where participants present their prototype solutions to a wider audience of potential customers and/or users from government, private sector or civil society through close coordination facilitated by MINICT and RISA.

Deliverables

Progress reports to be shared with CDO’s and Management
Technical documentations of PoC’s

In derogation from GIZ AVB, the contractor makes contributions to reports to GIZ’s commissioning party instead of submitting its own reports.

or:

In addition to the reports required by GIZ in accordance with the AVB, the contractor submits the following reports:

Inception report

Contributions to reports to GIZ’s commissioning party

Brief quarterly or half-yearly reports on the implementation status of the project (57 pages)

Certain milestones, as laid out in the table below, are to be achieved during the contract term:

Milestones/process steps/partial services	Deadline/place/person responsible
Kick-off meeting with GIZ, contractor and stakeholders	1 week after contract starts
Finalizing the needs assessment and skill gap analysis for project implementation	3 weeks after contract starts
Initial curriculum draft completed	1.5 months after contract starts
Curriculum finalized & Final Interim Report & Workplan	2 months after contract starts
Commencement of the training	2.5 months after contract starts
Conclusion of the training	7.5 months after contract starts
Participants’ presentation workshop – Prototype live demonstration of use case solutions (Per institution)	8 months after contract starts
Closing workshop-Certification & Reporting	8 months after contract starts
Course materials available & Handbook	9 months after contract starts
Evaluation after training	9 months after contract starts
Post Training Participant Evaluation Report	11 months after contract starts

Period of assignment: April 2025 until February 2026

2. Concept

In the tender, the tenderer is required to show how the objectives defined in Chapter 2 (Tasks to be performed) are to be achieved, if applicable under consideration of further method-related requirements (technical-methodological concept). In addition, the tenderer must describe the project management system for service provision.

Note: The numbers in parentheses correspond to the lines of the technical assessment grid.

Technical-methodological concept

Strategy (1.1): The tenderer is required to consider the tasks to be performed with reference to the objectives of the services put out to tender (see Chapter 1 Context) (1.1.1). Following this, the tenderer presents and justifies the explicit strategy with which it intends to provide the services for which it is responsible (see Chapter 2 Tasks to be performed) (1.1.2). At a top-level, this includes:

A broad curriculum draft including teaching methodology
Key prerequisites for participants applying for this program.

The tenderer is required to present the actors relevant for the services for which it is responsible and describe the cooperation (1.2) with them.

The tenderer is required to present and explain its approach to steering the measures with the project partners (1.3.1) and its contribution to the results-based monitoring system (1.3.2).

The tenderer is required to describe the key processes for the services for which it is responsible and create an operational plan or schedule (1.4.1) that describes how the services according to Chapter 2 (Tasks to be performed by the contractor) are to be provided. In particular, the tenderer is required to describe the necessary work steps and, if applicable, take account of the milestones and contributions of other actors (partner contributions) in accordance with Chapter 2 (Tasks to be performed) (1.4.2).

The tenderer is required to describe its contribution to knowledge management for the partner (1.5.1) and GIZ and to promote scaling-up effects (1.5.2) under learning and innovation.

Project management of the contractor (1.6)

The tenderer is required to explain its approach for coordination with the GIZ project. In particular, the project management requirements specified in Chapter 2 (Tasks to be performed by the contractor) must be explained in detail.

The tenderer is required to draw up a personnel assignment plan with explanatory notes that lists all the experts proposed in the tender; the plan includes information on assignment dates (duration and expert days) and locations of the individual members of the team complete with the allocation of work steps as set out in the schedule.

The Digital Transformation team will provide one contact person for the contractor. The explanations of the tenderer shall consider the following minimum standards to be met by the contractor:

The provision of at least one contact person for FF
The existence of substitution management
Daily availability by phone (on workdays)
Responding to emails within 24 hours
Weekly availability for physical or virtual meetings
Weekly progress reporting (verbal or written)
The submission of quality assured deliverables at least three workdays before the approval is due to be made (all deliverables need the approval from FF)

3. Personnel concept

The tenderer is required to provide personnel who are suited to filling the positions described, on the basis of their CVs (see Chapter 7), the range of tasks involved and the required qualifications.

The below specified qualifications represent the requirements to reach the maximum number of points in the technical assessment.

Team lead

Tasks of the team lead

Overall responsibility for the advisory packages of the contractor (quality and deadlines)
Ensure timely and quality delivery of key deliverables of work packages.
Coordinating and ensuring communication with GIZ, partners and others involved in the project
Personnel management, identifying the need for short-term assignments within the available budget, as well as planning and steering assignments and supporting local and international short-term experts
Providing team leadership and advisory to curriculum development and course implementation
Regular reporting in accordance with deadlines

Qualifications of the team lead

Education/training (2.1.1): Master's degree in education, IT, or other relevant field
Language (2.1.2): Proficiency in English language
General professional experience (2.1.3):7 years of professional experience focusing on training design, curriculum development or comparable; preferably in the intersection of learning, innovation and technology

Specific professional experience (2.1.4):

5 years' experience in artificial intelligence in the area of education / training and/or an academic setting
5 years of experience of developing curricula and/or training courses in the area of ICT or AI
3 years of experience managing public sector IT & data related projects

Leadership/management experience (2.1.5): 3 years of experience as project team leader or manager in a company

Regional experience (2.1.6): 3 years of working experience in Rwanda or in the East African region.

Development cooperation experience (2.1.7): Experience with at least 2 international development cooperation projects

Key expert 1: Curriculum development and AI Training Advisor

Tasks of the key expert1

Lead the development of tailored capacity-building programs for developers and senior developers in the public sector institutions of Rwanda, focusing on advanced data engineering techniques and AI.
Collaborate closely with local stakeholders and experts to understand specific requirements and challenges.
Design curriculum and training materials that address the needs of participants, incorporating best practices in data engineering and AI.
Provide guidance and support to the project lead in coordinating project activities related to training and capacity-building.
Offer mentorship and technical assistance to participants during training sessions and workshops.
Evaluate the effectiveness of training programs and suggest improvements based on feedback and outcomes.
Ensure timely reporting on training activities and progress to stakeholders and project management.
Facilitate in designing and supporting the developer’s teams in the POCs development phase.

Qualifications of key expert 1

Education/Training (2.2.1):Master's degree in computer science, data science, or a related field.
Language (2.2.2):Proficiency in English language. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.2.3):7 years of professional experience in data engineering and AI, with a focus on capacity-building and training.
Specific Professional Experience (2.2.4):
- 5 years of experience in developing and delivering training programs on data engineering and AI.
- 5 years of experience in data management, analytics, and machine learning techniques.
Leadership/Management Experience (2.2.5):3 years of experience in leading training initiatives or managing teams is preferred.
Regional Experience (2.2.6):Familiarity with the context and challenges of the public sector in Rwanda.

Key expert 2: Machine Learning Training Lead

Tasks of the expert 2: Machine Learning Training Lead

Lead the development and delivery of training programs on machine learning tailored to developers and senior developers in Rwandan public sector institutions.
Collaborate with local experts and stakeholders to identify training needs and design curriculum content.
Develop hands-on workshops and practical exercises to teach ML techniques & algorithms and provide guidance to participants during the implementation of projects
Assess the impact of training activities and recommend adjustments for continuous improvement.
Coordinate with project management and other team members to ensure alignment with project goals and timelines.
Encourage collaboration, knowledge sharing, and networking among participants.

Qualifications of expert 2:

Education/Training (2.3.1):Master's degree in applied machine learning, data science, computer science or related field.
Language (2.3.2):Proficiency in English language. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.3.3):5 years of experience as a professional facilitator / trainer, project manager, consultant or similar applying machine learning.

Specific Professional Experience (2.3.4):

5 years of experience in developing and implementing applied machine learning algorithms for real-world applications, including natural language processing, computer vision, and predictive analytics.
5 years of experience with one or moremachine learning frameworks and libraries (e.g., PyTorch, TensorFlow, scikit-learn, etc)
Prior experience in training or mentoring teams in machine learning concepts and applications
1 concrete example of designing and delivering technical training programs or workshops.

Regional Experience (2.3.6):- 2 years of experience of working on ML projects within the East African region.

Key expert 3: NLP Training Lead

Tasks of the expert 3: NLP Training Lead

Develop and implement a comprehensive training program focused on natural language processing (NLP) for developers and senior developers in public sector institutions in Rwanda.
Collaborate with local experts and stakeholders to tailor the curriculum to the specific needs and challenges of participants.
Design and deliver engaging training sessions and workshops on NLP techniques, algorithms, and applications.
Provide hands-on guidance and support to participants in applying NLP tools and frameworks to real-world problems.
Monitor participants' progress and provide feedback to ensure effective learning outcomes.
Liaise with the project lead and other team members to coordinate training activities and align project objectives.

Qualifications of expert 3

Education/Training (2.4.1):Master's degree in computational linguistics, natural language processing, or a related field.
Language (2.4.2):Proficiency in English language. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.4.3):5 years of professional experience in NLP research, development, or training.

Specific Professional Experience (2.4.4):

3 years of experience in developing NLP algorithms (experience with low-resource languages is an advantage)
5 years of experience with one or more deep learning software frameworks such as PyTorch, TensorFlow.
Prior experience with many of the following models in the context of NLP problems and data: Deep Learning (CNNs, RCNNs, LSTMs).

Regional Experience (2.4.6): 2 years of experience of working with the linguistic context and respective challenges of Rwanda.

Key expert 4: Lead Data Engineer

Tasks of the expert 4: Data Engineer Lead

Provide technical guidance and support to participants manipulating and processing different data infrastructures such as: data pipelines, ETL processes, and data integration solutions

Collaborate with local experts to identify training requirements and develop relevant curriculum content.
Develop hands-on workshops and practical exercises to teach data engineering techniques, tools, and best practices.
Assess participants' learning progress and provide constructive feedback to enhance their skills and competencies.
Evaluate the effectiveness of training activities and make recommendations for program improvements.
Coordinate with project management and other team members to ensure the successful delivery of training programs.

Qualifications of expert 4:

Education/Training (2.5.1):Master's degree in data science, computer science, or related field.
Language (2.5.2):Proficiency in English language. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.5.3):8 years of experience in data engineering, with a focus on designing and implementing data solutions.

Specific Professional Experience (2.5.4):

5 years of experience in data modeling, database design, and data architecture principles.
3 years of experience in leading data engineering processes and managing ETL processes for integrating diverse datasets from multiple databases
3 years of experience in programming languages like Python and SQL, and data processing frameworks (e.g., Spark).

Regional Experience (2.5.6): 2 years of experience leading local data infrastructure projects in Rwanda or within the East Africa region.

Key expert 5: Lead Business Intelligence Analyst

Tasks of the expert 5: Business intelligence Training Lead

Conduct training for participants from Rwandan public sector institutions on the development and maintenance of BI dashboards for real-time monitoring, or on utilizing the institutions' reporting software.
Collaborate with local experts and stakeholders to identify training needs and design curriculum content.
Develop hands-on workshops and practical exercises to teach techniques & algorithms and provide guidance to participants during the implementation of projects.
Provide expertise on how BI analysts work with IT/engineering teams to create and maintain data warehouses and other databases.
Coordinate with project management and other team members to ensure alignment with project goals and timelines.
Encourage collaboration, knowledge sharing, and networking among participants.

Qualifications of expert 5:

Education/Training (2.6.1):Master's degree in computer science, data science, business intelligence or related field.
Language (2.6.2):Proficiency in English language. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.6.3):5 years of experience as a professional BI analyst, project manager, consultant or similar applying machine learning.
Specific Professional Experience (2.6.4):
5 years of experience in developing and implementing data-driven solutions for real-world business applications, including process optimization, predictive analytics and decision-making support
3 years of hands-on experience with one or more BI tools (e.g., Tableau, Power BI or other date visualization platforms) to improve business processes and optimize data utilization and analysis
Prior experience in training or mentoring teams in business intelligence concepts and applications

Soft skills of team members

In addition to their specialist qualifications, the following qualifications are required of team members:

Team skills
Initiative
Communication skills
Socio-cultural skills
Efficient, partner- and client-focused working methods
Interdisciplinary thinking

Gender and diversity sensitiveness. Please note that the below-specified qualifications represent the ideal requirements to reach the maximum number of points in the technical assessment – not fulfilling the below-specified qualifications fully will not lead to an exclusion of the tenderer.

The below specified qualifications represent the requirements to reach the maximum number of points in the technical assessment.

Short-term expert pool with minimum 2, maximum 3 members

For the technical assessment, an average of the qualifications of all specified members of the expert pool is calculated. Please send a CV for each pool member (see below Chapter 7 Requirements on the format of the bid) for the assessment.

Task of the short-term expert pool

Contribute to the course design by offering specialized knowledge and guidance. The experts will ensure that the curriculum content is relevant, practical, and aligned with both academic rigor and the operational requirements of government institutions.
Develop and implement a comprehensive training program to address subject matter topics that emerged as necessary for public sector institutions in Rwanda. Topics may include, but are not limited to:
Natural language processing (NLP), Neural machine translation (NMT) and Conversational AI chatbots
Earth observation systems, satellite and drone data, collection of geospatial ground-truth data and machine learning for earth observation model building
Computer vision with a specialization in optical character recognition (OCR)
Advanced practices for deploying models on cloud services/platforms.
Liaise with the project lead and other team members to coordinate training activities and align project objectives on the short-term expert area of specialization.
Support of administrative and financial procedures including the sub-contract procedure according to GIZ regulations

Qualifications of the short-term expert pool

Education/Training (2.7.1): (Bachelor's, Master, PhD) degree in computer science with a specialization in data science, machine learning or related field.
Language (2.7.2): Proficiency in English language at C1 level. Fluent language proficiency in Kinyarwanda.
General Professional Experience (2.7.3): 4 years of professional experience in their respective areas of expertise, with demonstrated knowledge and skills in data analysis, advanced data science, NLP, ML4EO, CV or IT project management.

Specific Professional Experience (2.7.4):

1 expert with at least 3 years of experience working in an academic institution, specializing in the development of curricula related to machine learning topics and courses.
1 expert with 2 years of experience in data analysis and advanced data science: 1 concrete example of applying statistical and machine learning techniques to real-world problems, with proficiency in programming languages such as Python or R.
For NLP experts: At least 2 years of experience in developing NLP algorithms, frameworks, and applications, with a track record of 1 example of implementing NLP projects or initiatives. (experience with low-resource languages is an advantage)
For ML4EO experts: 3 years of professional experience in earth observation systems, satellite and drone data, collection of geospatial ground-truth data and machine learning for earth observation
For IT project management experts: 2 years of experience managing IT projects within the public sector context.

The tenderer must provide a clear overview of all proposed short-term experts and their individual qualifications.

4. Costing requirements

Assignment of personnel and travel expenses

Per diem allowances are reimbursed as a lump sum up to the maximum amounts permissible under tax law for each country as set out in the country table in the circular from the German Federal Ministry of Finance on travel expense remuneration (downloadable from the German Federal Ministry of Finance – tax treatment of travel expenses and allowances for international business travel as of 1 January 2024).

Accommodation allowances are reimbursed as detailed in the specification of inputs below.

With special justification, additional Accommodation costs up to a reasonable amount can be reimbursed against evidence.

All business travel must be agreed in advance by the officer responsible for the project

Sustainability aspects for travel

GIZ has undertaken an obligation to reduce greenhouse gas emissions (CO2 emissions) caused by travel. When preparing your tender, please incorporate options for reducing emissions, such as selecting the lowest-emission booking class (economy) and using means of transport, airlines and flight routes with a higher CO2 efficiency. For short distances, travel by train (second class) or e-mobility should be the preferred option.

CO2 emissions caused by air travel must be offset. GIZ specifies a budget for this, through which the carbon offsets can be settled against evidence.

There are many different providers in the market for emissions certificates, and they have different climate impact ambitions. The Development and Climate Alliance (German only) has published a list of standards (German only). GIZ recommends using the standards specified there.

Specification of inputs

Fee days	Number of experts	Number of days per expert	Total	Comments
Team leader	1	13	13
Expert 1: Curriculum development and AI Training Advisor	1	12	12
Expert 2: ML training Lead	1	28	28
Expert 3: NLP training Lead	1	28	28
Expert 4: Lead Data engineer	1	23	23
Expert 5: Lead Business Intelligence Analyst	1	28	28
Short-term expert pool			36	36 days altogether, will be split between the members of the expert pool
Other costs	Number	Price	Total	Comments
Flexible remuneration	1	4,300,000 RWF	4,300,000 RWF	A budget of 4,300,000 Rwf is earmarked for flexible remuneration. Please incorporate this budget into the price schedule. Use of the flexible remuneration item requires prior written approval from GIZ.
Workshops	1	10,800,000 RWF	10,800,000 RWF	The budget contains the following costs: o Training venue (including rent for equipment for remote teaching, e.g., camera, and microphone system, internet connection) o Transportation for participants
Procurement of materials and equipment	1	4,000,000 RWF	4,000,000 RWF	The budget contains the following costs: Computing power for participants for the duration of the training (i.e., AWS credits, licenses).

Workshops, events and trainings

Training Program

The contractor shall implement a 11-months AI training program in different phases and formats for aspiring AI professionals in the public sector. This program will accommodate a total of 20 participants, divided into two cohorts of 10 participants each. Each cohort will experience a 4-month training period, and both cohorts will follow the same delivery format, comprising training workshops and coaching sessions.

Training Workshops:Each cohort will start with a 1-month training period focused on AI fundamentals. This training may be delivered in a hybrid format (online and in-person), depending on agreements between instructors and participants. The contractor will arrange venues for group sessions, accommodating classes held twice a week for 2 hours each.
Coaching & Mentorship Sessions: Following the workshops, participants will engage in one-on-one coaching and mentoring sessions, concentrating on the implementation of their respective projects. This component of the program will extend over a period of 3 months. Given that the one-on-one sessions will solely depend on participants availability, the contractor will collaborate closely with GIZ to ensure the provision of an appropriate space or venue for these sessions.

Throughout the program, participants will be required to dedicate a minimum of 4 hours per week, divided into two 2-hour sessions on separate days. Training and coaching will be conducted at least twice a week. The overall training program will mount to a total of 2 months of classroom training, or at least 16 days, with an additional 48 days dedicated to coaching and mentorship on projects. No catering is required during the training program.

Procurement of materials and equipment

Throughout the training program, participants may require supplementary tools/equipment and software for project implementation, including:

Access credentials: ensuring participants have the necessary access to any online tools or platforms utilized during the training.
Software Licenses and computing resources (AWS, GCP): providing appropriate software licenses and computing power as needed for participants to practice hosting their projects.

5. Inputs of GIZ or other actors Whenever possible, GIZ will provide complimentary space and technical equipment for training and coaching sessions at the Digital Transformation Center in Kigali. Therefore, the contractor needs to coordinate closely with GIZ to schedule in-person sessions and select and book the appropriate venues, ensuring the most cost-effective solutions.

6. Requirements on the format of the tender

The structure of the tender must correspond to the structure of the ToR. In particular, the detailed structure of the concept (Chapter 3) should be organised in accordance with the positively weighted criteria in the assessment grid (not with zero). The tender must be legible (font size 11 or larger) and clearly formulated. It must be drawn up in English.

The complete tender must not exceed 10 pages (excluding CVs). If one of the maximum page lengths is exceeded, the content appearing after the cut-off point will not be included in the assessment. External content (e.g. links to websites) will also not be considered.

The CVs of the personnel proposed in accordance with Chapter 4 of the ToRs must be submitted using the format specified in the terms and conditions for application. The CVs shall not exceed 4 pages each. They must clearly show the position and job the proposed person held in the reference project and for how long. The CVs can also be submitted in English (

Please calculate your financial tender based exactly on the parameters specified in Chapter 5 Quantitative requirements. The contractor is not contractually entitled to use up the days, trips, workshops or budgets in full. The number of days, trips and workshops and the budgets will be contractually agreed as maximum limits. The specifications for pricing are defined in the price schedule.

7. Submission of the offer

Technical Proposal

A cover letter expressing your interest in this assignment
Self-declaration of eligibility for the award
Company registration certificate (RDB)
VAT registration certificate
Tax clearance certificate
Technical Proposal (attached template for technical proposal MUST be used)
Up to date CVs of proposed experts

7.3Financial offer:

Financial offer indicates the all-inclusive total contract price, supported by a breakdown of all costs as described in the specification of inputs. The costs must be in RWF and VAT excluded (Price sheet must be used).

Your EoI has to be submitted in 2 separated emails to RW_Quotation@giz.de until latest 17.03.2025:

The technical offerhas to be submitted in PDF format and as attachment to the email with the subject: 83483852-Technical offer.
The financial offerhas to be submitted in PDF format and as attachment to the email with the subject: 83483852-Financial offer.

If the emails exceed the default email size of 30MB, offers can be exceptionally submitted through https://filetransfer.giz.de/, as indicated. The subject of recipient notification must be edited with the subject “83483852-Technical/Financial offer“ and the notification message must include the password to access the files.