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 behaviorIdentify drop-offs, friction points, and conversion drivers in user journeysAnalyze campaign / channel performance and recommend optimizationsRun user segmentation, cohorting, and behavioral clusteringDesign structured frameworks for recurring decision-making3. Experimentation & Impact Measurement (across both)
Design and analyze A / B tests, rollouts, or quasi-experimentsApply statistical methods (diff-in-diff, uplift modeling, matching) to quantify impactOwn the measurement framework for product and incentive experimentsRequirements
2–3 years of hands-on experience in data science or ML-heavy analytics rolesProficient in Python (pandas, scikit-learn, statsmodels) and strong in SQLExperience building, evaluating, and iterating on ML models using real-world dataStrong understanding of statistics, experimentation, and causal inferenceComfortable translating business problems into data science workflowsExcellent communication — can explain models, assumptions, and trade-offs clearlyNice-to-Have
Experience with campaign modeling, churn prediction, fraud detection, or cashback optimizationExposure to lifecycle analytics or funnel deep divesFamiliarity with tools like MLFlow, Airflow, SageMaker, or containerization (Docker)Working knowledge of Redshift or AWS stackSome experience collaborating closely with product or marketing teamsTech Stack You’ll Use
Python – pandas, scikit-learn, NumPy, statsmodels, matplotlib / seabornSQL – RedshiftExperimentation – internal tools or Python-based frameworksData Infra – S3, internal data lakeCollaboration – Git, Slack, Notion, ConfluenceExample Projects
Build a predictive model to forecast LTV at the user onboarding stageDetect potential cashback fraud using behavioral clustering and thresholdsAnalyze drop-offs in key business flows and recommend high-leverage product fixesRun experiments on CRM campaign personalization and evaluate incremental ROICreate a user segmentation framework to power CRM and product targetingWhy This Role?
Real ML ownership : Your models will be used, not just prototypedDiverse challenges : Switch between modeling, experimentation, and analyticsHigh visibility : Work closely with founders and senior leadersBusiness impact : Influence product, growth, and financial outcomes directlyGrowth-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-solvingYou like real-world data, not clean toy datasetsYou enjoy going beyond model accuracy — to understand what it means and why it mattersYou can work independently, but communicate clearly with product, growth, and tech teamsWhy 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.