Fellowship Structure and Components

The India Data Capacity Accelerator gives social impact professionals and organisations vital tools for using data to address critical challenges at the intersection of climate and health. The goals of IDCA are achieved through three main pillars:

• Training a new generation of data practitioners with a focus on social impact.
• Connecting skilled practitioners with social sector organisations like government agencies and nonprofits to address critical, real-world problems.
• Boosting the ecosystem of data for social impact through the development of open-source curricula and templates for data governance, and unlocking new datasets.

IDCA’s academic partners train existing and aspiring data talent on harnessing, analysing, and applying data to policy problems. J-PAL SA then curates one-year fellowship opportunities for a select group of professionals, placing them in social impact organisations on innovative projects leveraging climate and health data.

This approach builds on J-PAL SA’s over 17 years of experience working collaboratively with ecosystem partners to ensure that policy is informed by scientific evidence.

• Projects in Climate and Health: Opportunity to apply advanced data science skills to address real-world problem statements and learn under experienced practitioners and experts at a leading social impact organisation.
• Upskilling: All fellows undergo a structured journey of growth under a Continuous Development Plan. The initiative is an integral component of the programme that focuses on providing diverse avenues of upskilling activities for fellows and interns such as peer learning, mentorship, workshops, and modular courses in relevant topics. 
• Mentorship: Throughout the fellowship, each IDCA fellow is guided by mentors (industry experts) from climate and/or health domains and data fields. The mentorship programme provides valuable guidance and expertise, enhancing fellows’ capabilities as they address complex challenges. Fellows gain practical skills by leveraging their mentors’ insights and experiences, while also expanding their professional networks through meaningful connections with seasoned experts. Mentors, in turn, contribute to shaping the next generation of data professionals and engage with innovative problem statements in climate and health. Additionally, IDCA seeks to build a robust network of mentors dedicated to addressing challenges at the intersection of climate and health by leveraging data.
• Networking: The fellowship programme hosts a vibrant community of like-minded data professionals that can support each other in their respective learning goals and aspirations through one-on-one connections and group activities. All fellows are encouraged to engage with peers and mentors to promote exchange of learnings and reflections, ensuring there is a sense of connectedness and collective growth through digital platforms hosted by WHO HIVE. 

The execution of the fellowship model is strategically guided by an Advisory Committee curated by J-PAL SA composed of distinguished members who bring together a diverse range of expertise, ensuring comprehensive guidance to the programme. This expertise includes deep domain knowledge in climate, health, and data science, strong networks in nonprofit and government sectors, and expertise in training and capacity-building.

In its essential role, the Advisory Committee’s involvement spans multiple dimensions crucial to the success of IDCA:

1. Partner Engagement: Advisory Committee members advise on the outreach plan to partners and actively facilitate connections to cultivate new collaborations, broadening IDCA’s network. They review partner criteria and the identification of fellowship partners, ensuring alignment with programme goals.
2. Project Review: Committee members assist in regularly reviewing project criteria to ensure meaningful and impactful projects that align with the programme’s climate and health objectives. They review project proposals submitted by fellowship partners and offer valuable insights into the overall progress of different projects as needed.
3. Fellow Selection: The committee reviews the identification and matchmaking processes for fellows. Additionally, their expertise aids in devising a robust mentoring model that fosters fellows’ growth.
4. Strategic Oversight: Overall steering of the programme to advise future strategy and goals of the India Data Capacity Accelerator.

Selection Criteria for Fellows

Through the India Data Capacity Accelerator, data fellows are not only trained to analyse and interpret complex data but also equipped with the critical skills to communicate their insights effectively. By bridging the gap between data and decision-making, the fellowship empowers fellows to translate data into actionable narratives, enabling the larger ecosystem to make informed, evidence-based decisions that drive impact in the climate and health space.

In order to filter applications and identify profiles compatible with the programme, a detailed process with several rounds of assessments has been designed. The process deliberately ignores limits and preferences around age and prior academic scores to provide candidates from varied backgrounds, functions, and competencies with a fair chance of selection based on their present interests and skills.

These rounds have been built to assess candidates’ proficiency from varied perspectives and frames of reference, thereby minimising the probability of attrition and dissatisfaction amongst fellows, as well as maximising value to both fellows and partner organisations. The fellowship selection process includes the following rounds and takes approximately two months to complete. 

Round 1: Preliminary Screening and Technical Assessment

• Statement of purpose to assess the candidate’s ability to communicate their background, experience, and aspirations clearly.
• Technical assessment with questions related to the data lifecycle including management, analysis, visualisation, security. Difficulty level of the assessment (easy / medium / difficult) shared will depend on the technical background and exposure of the candidate.
• Non-technical assessment related to relationship building and management, attitudes, values, process review and understanding, and logical thinking.

Round 2: Interviews

• Interview with representatives from J-PAL SA and data.org (Evaluates the candidate’s overall fit for the programme, including their motivation, alignment with programme objectives, and broader perspectives).
• Interview with host organisation (Assesses the candidate’s suitability for the specific fellowship project by evaluating their skills, experiences, and potential contributions to the organisation).

The processes followed in these selection rounds ensure that selected fellows possess the right skills, experiences, and motivations to effectively contribute to projects and learn practical application of data science concepts. In particular, the selection criteria for an ideal fellow encompasses the following parameters:

1. Commitment to Social Impact
2. Analytical Thinking
3. Technical Skills
4. Ethical Considerations
5. Project Management
6. Communication Skills
7. Collaboration and Teamwork
8. Continuous Learning

Additionally, when devising and deploying these criteria, J-PAL SA aimed to strike a balance between the uniqueness of each fellow’s profile, their learning goals, and the specific demands of the projects to which they would be matched. To accommodate varying levels of data maturity and sectoral focus of different partner organisations, the criteria included a range of attributes that can enable fellows to deliver regardless of their background, or the organisation’s data readiness. 

Selection Criteria for Partners and Projects

On the demand side, J-PAL SA strategically engaged with stakeholders demonstrating a strong willingness to solve emerging problems in climate change and public health. Thus, the criteria used to shortlist the right set of partners as host organisations involved a clear mission alignment along with several other parameters such as:

• Domain Alignment and Community-Orientation: The organisations had extensive experience working on issues related to public health and/or climate change and sustainability in collaboration with communities and policymakers. Their approach reflected a clear commitment to fostering climate and health solutions deeply rooted in local contexts. 
• Commitment Towards Climate Change-Health Nexus: The organisations were able to demonstrate a clear commitment or willingness towards participating in research related to the health-climate change nexus in the coming years. This criterion stemmed from the recognition that knowledge about this field is nascent and IDCA aims to expand the same. However, recognising the diversity in mission statements of organisations, the criterion also took into consideration other priorities that are aligned with deepening work in the relevant thematic areas.
• Strong Existing Data Structures and Capabilities: The organisation either had an existing team that works on data functions or had a detailed plan for efficient and effective data management including collection, cleaning, analysis, visualisation, interpretation and use.
• Diversity and Inclusion Considerations: The programme strove to ensure diversity among the partners in terms of several indicators such as geography (location of the organisations and their projects), size, data capacity and maturity, and sector (nonprofit, public, private).

The project selection criteria served as a framework to evaluate the feasibility, impact, and alignment of projects with IDCA’s mission, the partner organisations’ objectives and the fellows’ capabilities. There were several key considerations: the potential for research, data utilisation, and long-term sustainability, ensuring that the projects not only address immediate challenges but contribute to broader learning and development in the climate and health domains. Lastly, it was assessed whether the project meaningfully engages the skills of the fellows, sparking their interest in furthering a career in the social impact sector. 

Broadly, parameters that can be used as a checklist for projects prior to bilateral discussions are below. (Note: It is not necessary for a project to fulfill all conditions in this list)

• Project Theme: The project should be related to health, climate change, and/or the intersectionality of these two fields with the explicit goal of using data and research to investigate a problem, design and test solutions, and/or inform and scale successful data-driven solutions.
• Data Availability & Quality: The project should have a clear plan and protocol for data sources, collection (if any), management (including cleaning, linking, etc.), and use.
• Scalability & Replicability: The project has the potential to scale, or be replicated within the organisation or in other contexts in the future. The fellows’ work should ideally contribute to building sustainable data practices within the organisation, allowing it to continue benefiting from the data analysis even after the fellowship ends.
• Feasibility & Timeline: The project should be feasible within the one-year fellowship period, keeping in mind the fellows’ skills, the available organisational resources, and the complexity of the problem statement to be addressed. Alternatively, there should be a plan in place for how the project will be transitioned and continued.
• Ethical Considerations: Projects must adhere to rigorous standards of privacy protection, ethical conduct, and data security, ensuring the responsible handling and safeguarding of sensitive information throughout the project lifecycle. The project execution and outcomes should account for possible risks of harm to all stakeholders involved, and outline a plan to manage those risks.
• Leadership Priority & Sustainability: The project should be of priority to the organisational leadership and have sufficient human, financial, and technology resources from the organisation for its implementation over the next three years (or a plan to raise the necessary resources for the same). 
• Experiential Learning Opportunity: The project should offer meaningful learning opportunities for fellows, taking into account their skills and learning goals. Such sector experiences will complement fellows’ formal training, keep fellows engaged, and have the potential to generate lasting interest in career pathways in data science for social impact.

In the pursuit to promote rigor in the project curation process, J-PAL SA also devised a comprehensive scoring system that quantifies key features from the partner and project criteria. This approach guides us towards projects that demonstrate promise across multiple dimensions – from innovation and sustainability to capacity building and skill enhancement. The scoring methodology is structured around three core pillars:

1. Leveraging Data Science for Climate & Health: This pillar scrutinises the project with regards to its positioning within the climate-health nexus, its utilisation of data and tools, and its capacity to result in scalable solutions.
2. Building Capacity of Social Impact Organisations (SIOs): This pillar evaluates the alignment of the organisation with the overarching vision of CAN, and the strategic significance of the fellowship project for the larger ecosystem.
3. Deep and Sustainable Engagement for Fellows: This pillar evaluates the suitability of the fellows’ skills, motivation, and alignment with the project.

Matchmaking Process

Once selected, fellows are matched with project opportunities that complement their formal training and provide them with meaningful opportunities to apply their skills and experiences. Apart from foundational knowledge of statistics and data science, these opportunities benefit from a candidate’s domain knowledge in the concerned thematic areas, enabling them to identify and define research goals in collaboration with the host organisation and work with complex datasets consisting of a myriad of climatic, health, and socioeconomic indicators. Therefore, a balanced approach was adopted in mapping a candidate to a particular project where both their data skills and domain expertise through prior professional experience are factored into the decision making process. 

Matchmaking also depends in large part on how the candidate is placed within three broad categories of professional experiences given below:

Broad Fellow Categorisation

1. New DSI Talent: Very new to the workforce or entering for the first time, but with moderate-to-high exposure to data.
2. Upskilled Social Sector Talent: Professionals already working in various capacities across the social sector with strong data intuition and with a desire to embrace data roles, especially in health or climate change fields.
3. Transitional Data Science Talent: Experienced data professionals from private or public sectors seeking to transition into social sector roles, ideally with a project background promoting data-driven decision-making.

For instance, requirements for hard technical skills can differ based on the background and experience of the applicant and requirements of the host organisation. Applicants with extensive social impact experience and/or strong domain expertise will be required to demonstrate relatively fewer or less advanced technical skills. 

The parameters considered in the matchmaking process are as follows:

Parameter	Description
Technical Skills	Assess the specific technical requirements and skill sets needed for each fellowship project and evaluate alignment with the data fellows’ expertise.
Domain Knowledge	Knowledge and experience in domain(s) relevant to the fellowship project.
Project Complexity & Leadership Potential	Aligns with their level of experience and ability to handle project complexity, ranging from entry-level to advanced challenges. Based on leadership qualities, initiative-taking abilities, and capacity to drive project objectives independently, especially for projects that require a high level of autonomy and self-direction.
Geographic Location and Logistics	Consider practical factors such as the geographic location of the fellowship projects and logistical considerations for the data fellows, including travel arrangements, remote work options, and any other constraints that may impact their participation in the projects.
Personal Interests and Motivation	Take into account personal interests, career aspirations, and motivation to work on specific types of projects or with certain organisations, ensuring a strong match between their individual goals and project opportunities.