#DataForImpact: Insights from Anuja Venkatachalam’s Fellowship Journey

Could you tell us a bit about yourself and your journey before joining the fellowship?

AV : I started as a research associate at J-PAL in 2015, working on randomized controlled trials (RCTs) and scoping studies. My projects initially revolved around field-based interventions, like evaluating the effectiveness of maternal anemia and nutrition initiatives. However, my transition into data science began in 2019 when I worked on an edtech RCT evaluating MindSpark, a learning software by Educational Initiatives. That project exposed me to data analysis at scale, sparking my interest in using Python, R, and open-source tools. It was during this period that I realized the need to build my technical skills further, which led me to pursue a diploma in computer science at Columbia University. Eventually, I returned to J-PAL to work on the IDEA initiative, where I focused on helping government departments leverage administrative data. When the IDEA initiative concluded, I transitioned into the IDCA Fellowship, blending my research background with my passion for data science.

Was your transition from J-PAL to IDCA seamless? How did J-PAL support you during the fellowship?

AV:? J-PAL played a significant role in shaping my journey and preparing me for the fellowship. The culture of innovation and experimentation at J-PAL really fostered my interest in data science. During my time on the edtech RCT, the principal investigators encouraged exploring unconventional methods, allowing me to dive deeply into software interventions and data analysis. That experience was pivotal in sparking my interest in this field.

Moreover, J-PAL’s support, especially during the IDEA initiative, was instrumental. The team recognized my unique skill set, which bridged data science and research, and they encouraged me to take on this new role. While the transition to IDCA was a learning curve, having J-PAL’s backing and their emphasis on interdisciplinary work made it much smoother. Their commitment to fostering professional growth and equipping me with the right tools and opportunities set a strong foundation for my journey into the fellowship.

What makes the IDCA Fellowship unique, and what motivated you to apply?

AV: The fellowship stands out because it provides social impact professionals the opportunity to transition into data science roles, a pathway not commonly available in the sector. For me, the chance to work with organizations like ARTPARK on defined, impactful projects was a key motivator. Unlike the broader scope of administrative data use, the fellowship offered a structured environment where I could work directly on data science projects aligned with real-world challenges.

How has the fellowship shaped your understanding of the intersection between climate and health?

AV: Although my project at ARTPARK did not have a direct climate focus, it incorporated climate datasets for dengue predictions. Working with weather data introduced me to the nuances of data aggregation and resolution. Additionally, collaborating with a team primarily composed of scientists at the Indian Institute of Science exposed me to concepts like sero-surveillance and the scientific underpinnings of dengue. These experiences deepened my appreciation for how data science can intersect with public health and climate resilience.

Can you walk us through your project at ARTPARK? How does it address a specific Climate & Health challenge?

AV: At ARTPARK, I worked on improving dengue prediction models, a crucial tool for guiding public health interventions. The team had already developed a risk assessment model that generated weekly risk scores for districts, helping the government of Karnataka allocate resources and implement response strategies like door-to-door surveys and information campaigns.

My role focused on building robust data pipelines, cataloging datasets, and standardizing data quality checks. This involved documenting every dataset, indexing and uploading them to a cloud platform, and creating an automated system to preprocess and standardize data. These steps ensured the data’s integrity, streamlined operations, and made the model’s predictions more reliable.

Additionally, the data engineering infrastructure we developed allowed us to integrate and reconcile new datasets, such as publicly available information on water pipelines, enriching the existing data. While my work wasn't directly tied to climate change, it contributed to improving the scalability of predictive models to other diseases and states. By addressing inefficiencies in data management, we not only enhanced the dengue model’s efficiency but also created a replicable framework that other organizations could adopt to tackle similar health challenges influenced by environmental factors.

What challenges did you face during the fellowship, and what are your key learnings?

AV: One major challenge was aligning my work on data engineering with the fellowship's broader objectives. Additionally, explaining abstract models to policymakers and field officers required simplifying technical details. These experiences taught me the importance of transparency and adaptability in data-driven policymaking. On a personal level, the fellowship reinforced the value of having both technical and contextual knowledge in data science.

As someone deeply involved in this field, what’s your perspective on women’s representation in data science, and what steps can be taken to encourage more women to join initiatives like this?

AV: During the fellowship, I was the only woman on my team—a stark contrast to J-PAL's balanced environment. This reflects broader societal trends, where tech fields still skew male-dominated due to systemic barriers and social conditioning. However, programs like the IDCA Fellowship are pivotal in addressing these gaps by offering accessible pathways into data science for women from diverse backgrounds.

Providing opportunities for capacity-building, especially for individuals without formal technical education, is crucial. Fellowships like IDCA can demystify data science by offering real-world exposure and structured learning environments. Encouraging participation from non-engineering backgrounds and setting clear expectations creates a more inclusive and equitable environment. Additionally, fostering supportive networks and highlighting role models within data science can inspire more women to explore and thrive in this field. These collective efforts are essential to shifting the gender balance and redefining representation in tech.

Looking ahead, what’s next for you, and how has the fellowship shaped your career trajectory?

AV: The fellowship helped me clarify my focus between data science and data engineering. Moving forward, I’m leaning towards data science roles while appreciating the foundational importance of data engineering. Currently, I’m pursuing a degree in programming and data science at IIT Madras, which complements the skills I’ve gained through the fellowship. This experience has been instrumental in preparing me for a career at the intersection of data science and social impact.

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