Data Scientist

Company Overview : CashKaro

is India’s #1 cashback platform, trusted by over 25 million users! We drive more sales for Amazon, Flipkart, Myntra, and Ajio than any other paid channels, including Google and Meta. Backed by legendary investor Ratan Tata and a recent $16 million boost from Affle, we’re on a rocket ship journey—already surpassing ₹300 crore in revenue and racing towards ₹500 crore.

EarnKaro , our influencer referral platform, trusted by over 500,000 influencers, sends more traffic to leading online retailers than any other platform. Whether it’s micro-influencers or top- tier creators, they choose EarnKaro to monetize their networks. Here at EarnKaro, we empower influencers, content creators, and everyday users to monetize their reach by sharing affiliate links.

BankKaro

is India’s fastest-growing platform for saving smartly on banking and finance products. We help users get the best deals on credit cards, loans, and more — while earning rewards.

Role Summary

We’re hiring a Data Scientist who blends machine learning capability with strong analytical thinking to solve business-critical problems.

This is a 50 : 50 hybrid role — half your time will go into building and refining ML models (LTV, fraud, targeting, Churn, etc.), and the other half into product analytics, deep-diving funnels, retention, and growth patterns to identify levers for scale.

You’ll work closely with the Head of Data & Analytics and collaborate with Product, CRM, Marketing, and Tech teams to convert ambiguity into models, experiments, and insights that influence ₹1000Cr+ business decisions.

What You'll Work On

1. Applied Data Science (50%)

Build machine learning models for LTV prediction, fraud detection, churn scoring, etc.

Do exploratory data analysis, feature engineering, algorithm tuning, and validation

Translate business goals into modeling problems and take them end-to-end (POC to production-readiness)

Collaborate with engineering teams to deploy models and track real-world performance

Automate retraining pipelines, monitoring, or model risk assessment

2. Product & Growth Analytics (50%)

Deep-dive into funnels, retention curves, feature usage, and cashback behavior

Identify drop-offs, friction points, and conversion drivers in user journeys

Analyze campaign / channel performance and recommend optimizations

Run user segmentation, cohorting, and behavioral clustering

Design structured frameworks for recurring decision-making

3. Experimentation & Impact Measurement (across both)

Design and analyze A / B tests, rollouts, or quasi-experiments

Apply statistical methods (diff-in-diff, uplift modeling, matching) to quantify impact

Own the measurement framework for product and incentive experiments

Requirements

2–3 years of hands-on experience in

data science or ML-heavy analytics roles

Proficient in

Python (pandas, scikit-learn, statsmodels)

and strong in

SQL

Experience building, evaluating, and iterating on ML models using real-world data

Strong understanding of statistics, experimentation, and causal inference

Comfortable translating business problems into data science workflows

Excellent communication — can explain models, assumptions, and trade-offs clearly

Nice-to-Have

Experience with campaign modeling, churn prediction, fraud detection, or cashback optimization

Exposure to lifecycle analytics or funnel deep dives

Familiarity with tools like MLFlow, Airflow, SageMaker, or containerization (Docker)

Working knowledge of Redshift or AWS stack

Some experience collaborating closely with product or marketing teams

Tech Stack You’ll Use

Python

– pandas, scikit-learn, NumPy, statsmodels, matplotlib / seaborn

SQL

– Redshift

Experimentation

– internal tools or Python-based frameworks

Data Infra

– S3, internal data lake

Collaboration

– Git, Slack, Notion, Confluence

Example Projects

Build a predictive model to forecast LTV at the user onboarding stage

Detect potential cashback fraud using behavioral clustering and thresholds

Analyze drop-offs in key business flows and recommend high-leverage product fixes

Run experiments on CRM campaign personalization and evaluate incremental ROI

Create a user segmentation framework to power CRM and product targeting

Why This Role?

Real ML ownership : Your models will be used, not just prototyped

Diverse challenges : Switch between modeling, experimentation, and analytics

High visibility : Work closely with founders and senior leaders

Business impact : Influence product, growth, and financial outcomes directly

Growth-stage momentum : Help shape data science as we scale to ₹1000Cr+

This Role is for You If…

You want to spend 50% of your time building models, but still love structured problem-solving

You like real-world data, not clean toy datasets

You enjoy going beyond model accuracy — to understand what it means and why it matters

You can work independently, but communicate clearly with product, growth, and tech teams

Why Join Us

Work on real problems : Your insights won’t sit in slides — they’ll drive experiments, campaigns, and roadmaps.

High visibility : Work directly with functional leaders and founders.

Cross-functional exposure : Collaborate with marketing, CRM, product, and operations teams.

Business-first environment : We reward impact — not just activity or output.

Fast-growth company : Be part of a rocket ship scaling toward ₹500 Cr+ in revenue.