About Customer Capital

Customer Capital builds contemporary loyalty-led commerce platforms for large enterprises. We power captive shopping, travel, gifting, and personalization ecosystems for leading banks and partners, helping them drive engagement, frequency, and monetization across their customer base. Our products sit at the intersection of commerce, loyalty, and data — and our ability to understand, score, and predict user behavior is central to every product decision we make.

The Role

We are looking for a Data Scientist who sits at the intersection of rigorous modeling and sharp business instinct. You will own the full analytics stack — from stitching raw data across diverse sources into a reliable warehouse, to building the dashboards, scoring systems, and predictive models that tell us what is happening, why, and what to do next.

You will be embedded with product and growth teams, and your models will ship into production decisions — who gets recommended what, which users are at risk, where the next experiment should run. You are expected to use AI tools to move fast; this is a baseline expectation, not a bonus.

Key Responsibilities

Data Infrastructure & Warehousing

- Stitch together data from diverse sources — product events, CRM, payments, loyalty, and third-party feeds — into a coherent, reliable data warehouse on BigQuery
- Design and maintain clean data models that product, marketing, and leadership can trust
- Own data quality — find and fix issues before anyone else notices them

Dashboards & Insight Generation
- Build and own dashboards that give stakeholders a clear, real-time view of the metrics that matter




- Go beyond reporting — proactively surface patterns, anomalies, and opportunities without being asked
- Translate complex data into crisp, actionable narratives for non-technical audiences

User Scoring Algorithms

- Design and deploy scoring models that rank users by engagement, health, churn risk, or lifetime value
- Identify the behavioral signals that predict outcomes and build systems that act on them
- Continuously validate and recalibrate scores as user behavior evolves

Predictive Modeling, Recommendations & Experimentation
- Build propensity models and recommendation engines that drive personalization and measurable outcomes
- Design and run A/B and multivariate tests with proper incrementality measurement — no vanity wins
- Drive an ongoing test-and-learn framework: exploit what is working, explore what is unknown
- Stay current on causal inference, uplift modeling, and experiment design — apply where relevant

Must-Have Skills

- Python — data wrangling, modeling, and productionizing; fluency across pandas, scikit-learn, and at least one ML framework
- BigQuery & SQL — complex queries, query optimization, working at scale; BigQuery should feel like home
- Data Visualization — Streamlit, Metabase, Tableau, or equivalent; ability to design dashboards that actually get used




- Statistical rigor — you know when a result is real; A/B testing, confidence intervals, and experiment design are second nature

Good to Have

- Experience with data transformation frameworks
- Familiarity with recommendation systems — collaborative filtering, embedding models
- Exposure to MLflow or comparable experiment tracking and model management
- Working knowledge of causal inference methods — diff-in-diff, synthetic control, uplift modeling
- Experience in e-commerce, travel, loyalty, or fintech consumer domains

The Ideal Candidate

- 3–6 years in a data science or senior analytics role within a product or growth team at a consumer internet company
- You have shipped models that influenced real product decisions — not just slide decks
- Comfortable owning the full lifecycle: hypothesis → data → model → dashboard → decision
- You use AI tools — Copilot, Claude, Cursor, or equivalent — as a force multiplier in your daily work
- You ask 'so what?' before considering any analysis done
- Strong communicator — you can explain model outputs to a PM without dumbing it down

What Success Looks Like

- A trusted, well-structured data warehouse that teams rely on daily
- Dashboards and scoring systems adopted and acted upon by business stakeholders
- Predictive models that demonstrably shift user outcomes — retention, conversion, or engagement
- An always-on experimentation framework that separates real signal from noise
- AI tools used effectively to accelerate the pace and quality of analytical output

Location & Work Mode

Location: Mumbai / Hybrid

📌 Data Scientist (Mumbai)
🏢 Customer Capital
📍 Mumbai