Lead Data Science & Analytics
About the Role
This is a foundational role in our Analytics journey. You will be the first dedicated Data Science leader at SarvaGram - embedded within Technology and working directly with the CTPO. You will own the end-to-end analytics charter: from understanding our data landscape and identifying high-impact problems, to building models that drive measurable business outcomes across employee performance, portfolio health, and early warning systems.
Reports to: Chief Technology & Product Officer (CTPO)
You will operate as a player-coach - doing hands-on data science work while mentoring a growing team of data scientists and analysts.
Role Overview
We are looking for an Analytics Lead to build and own SarvaGram’s analytics and data
engineering layer from the ground up. This role will be responsible for creating a single source
of truth, enabling business-critical decision making, and laying the foundation for scalable,
compliant, and trustworthy analytics across the organization.
This is a hands-on leadership role requiring strong data engineering fundamentals, business
acumen, and the ability to work closely with Product, Risk, Operations, and Leadership.
Key Responsibilities
1. Problem Discovery & Analytics Roadmap
• Conduct a structured discovery of data assets across all business verticals (lending,
collections, HR, operations)
• Identify and prioritize high-value use cases for data science and analytics across three core
domains:
• Branch performance - productivity, attrition prediction, workforce planning
• Portfolio performance - cohort analysis, yield analytics, repayment behaviour
• Early warning & stress models - delinquency prediction, credit stress indicators, collection
triggers
• Define and own the analytics roadmap in alignment with the key stakeholders
• Translate ambiguous business questions into well-framed data science problem statements
2. Hands-on Modelling & Analysis
• Design, develop, validate, and deploy statistical and machine learning models end-to-end
• Build credit risk scorecards, early warning models, and portfolio stress frameworks suited to
NBFC lending portfolios
• Work with partially structured data - perform data wrangling, feature engineering, and
pipeline development using Python and SQL
• Leverage Snowflake and AWS infrastructure for scalable data processing and model
deployment
• Ensure models are explainable, interpretable, and regulator-friendly - a critical requirement in
the NBFC context
3. Team Building & Mentorship
• Onboard, mentor and guide a team of data scientists and analysts, setting technical
standards and reviewing work
• Define best practices for modelling, code quality, documentation, and experimentation
• Contribute to hiring decisions as the team grows
• Foster a culture of curiosity, rigour, and business impact within the analytics function
4. Stakeholder Collaboration
• Partner closely with Risk, Credit, Collections, HR, and Business Vertical heads to understand
domain needs
• Communicate findings, model outputs, and recommendations clearly to non-technical
stakeholders
• Create dashboards and reports in Metabase or equivalent BI tools to democratise data
insights
• Work alongside data and engineering teams to improve data quality and availability
Requirements
Academic Qualification:
Strong foundation in Statistics, Mathematics, or a related quantitative discipline is essential. We are
looking for candidates with the academic rigour of programmes such as:
• M.Sc. / M.Stat. / Ph.D. in Statistics from premier institutes (IISc, ISI Kolkata/Delhi, IITs, CMI,
IISER)
• M.Tech. in AI/ML, Data Science, or related fields from IITs / IISc
• Equivalent strong quantitative background from internationally recognised institutions
Candidates with a deep understanding of probability theory, statistical inference, stochastic
processes, and applied mathematics will have a strong advantage
Professional Experience:
• 7 to 12 years of progressive experience in data science and analytics roles
• Mandatory: Prior exposure to the NBFC, banking, or broader financial services/fintech
domain
• Hands-on experience building credit risk models, scorecards, or financial stress indicators
• Experience working in environments with partially structured or fragmented data — not just
clean, modelling-ready datasets
• Prior experience in a player-coach capacity — having independently led projects while
guiding junior team members
Technical Skills:
• Python (primary language) - proficient in pandas, NumPy, scikit-learn, statsmodels
• SQL - strong querying, data preparation, and transformation skills; Snowflake experience
preferred
• AWS - familiarity with S3, EC2, SageMaker or equivalent ML tooling on cloud
• Statistical Modelling - logistic regression, survival analysis, time-series forecasting, Bayesian
methods
• Classical ML - decision trees, ensemble methods, scorecards, regularised regression
• Advanced ML - gradient boosting, neural networks, NLP (advantageous, not mandatory)
• Model Explainability - SHAP values, LIME, or other interpretability frameworks
• BI Tools - Metabase, Tableau, Power BI, or similar; ability to build self-serve dashboards
• Version control, experiment tracking, and collaborative development practices (Git, MLflow,
etc.)
Soft Skills & Mindset:
• Strong problem framing ability - comfort with ambiguity and the ability to define structure
where none exists
• Business orientation - translates data insights into decisions and outcomes, not just metrics
• Communication - can explain complex modelling concepts clearly to business and risk
stakeholders
• Ownership mindset - takes end-to-end accountability from problem definition through to
model deployment
• Collaborative and cross-functional - comfortable working across Risk, Credit, Collections,
HR, and Technology
• Intellectually rigorous - high standards for statistical validity, model robustness, and
documentation
• Strong problem framing ability - comfort with ambiguity and the ability to define structure
where none exists
• Business orientation - translates data insights into decisions and outcomes, not just metrics
• Communication - can explain complex modelling concepts clearly to business and risk
stakeholders
• Ownership mindset - takes end-to-end accountability from problem definition through to
model deployment
• Collaborative and cross-functional - comfortable working across Risk, Credit, Collections,
HR, and Technology
• Intellectually rigorous - high standards for statistical validity, model robustness, and
documentation
What Makes This Role Unique:
• Greenfield mandate - You define the analytics practice from scratch - no
legacy frameworks to work around
• CTO visibility - Direct reporting line means your work shapes the organisation's data strategy at the highest level
• Real-world complexity - Work with messy, multi-vertical, real lending data - not sanitised Kaggle datasets
• Mission-driven context - Every model you build ultimately improves financial access for rural households
• Team building opportunity - Be the first leader of an analytics team that will grow with the organisation
• CTO visibility - Direct reporting line means your work shapes the organisation's data strategy at the highest level
• Real-world complexity - Work with messy, multi-vertical, real lending data - not sanitised Kaggle datasets
• Mission-driven context - Every model you build ultimately improves financial access for rural households
• Team building opportunity - Be the first leader of an analytics team that will grow with the organisation
Benefits
SarvaGram is on a mission to revolutionize financial services for millions in rural India. We're
building the nation's first data-driven platform that combines cutting-edge technology with a
human touch to unlock financial possibilities for underserved households.
This is your chance to be at the forefront of innovation. Join us and:
• Shape the future of FinTech: We're not just building a product, we're creating a new
category. Be a part of defining the future of financial inclusion for rural India.
• Embrace a high-growth, high-impact environment: This is a non-linear growth
opportunity. Build a platform used by millions and witness the network effect drive
massive scale.
• Tackle real-world challenges: Apply your skills to solve critical problems and directly
empower rural communities.
• Craft solutions that touch lives: Develop innovative products used by diverse household
members, each with unique needs