Data Scientist (Product company, Mumbai)

Review Criteria

• Strong Data Scientist/Machine Learnings/ AI Engineer Profile
• 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models
• Strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.
• Hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models
• Strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text
• Experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments
• Preferred (Company) – Must be from product companies

Job Specific Criteria

• CV Attachment is mandatory
• What's your current company?
• Which use cases you have hands on experience?
• Are you ok for Mumbai location (if candidate is from outside Mumbai)?
• Reason for change (if candidate has been in current company for less than 1 year)?
• Reason for hike (if greater than 25%)?

Role & Responsibilities

• Partner with Product to spot high-leverage ML opportunities tied to business metrics.
• Wrangle large structured and unstructured datasets; build reliable features and data contracts.
• Build and ship models to:
• Enhance customer experiences and personalization
• Boost revenue via pricing/discount optimization
• Power user-to-user discovery and ranking (matchmaking at scale)
• Detect and block fraud/risk in real time
• Score conversion/churn/acceptance propensity for targeted actions
• Collaborate with Engineering to productionize via APIs/CI/CD/Docker on AWS.
• Design and run A/B tests with guardrails.
• Build monitoring for model/data drift and business KPIs

Ideal Candidate

• 2–5 years of DS/ML experience in consumer internet / B2C products, with 7–8 models shipped to production end-to-end.
• Proven, hands-on success in at least two (preferably 3–4) of the following:
• Recommender systems (retrieval + ranking, NDCG/Recall, online lift; bandits a plus)
• Fraud/risk detection (severe class imbalance, PR-AUC)
• Pricing models (elasticity, demand curves, margin vs. win-rate trade-offs, guardrails/simulation)
• Propensity models (payment/churn)
• Programming: strong Python and SQL; solid git, Docker, CI/CD.
• Cloud and data: experience with AWS or GCP; familiarity with warehouses/dashboards (Redshift/BigQuery, Looker/Tableau).
• ML breadth: recommender systems, NLP or user profiling, anomaly detection.
• Communication: clear storytelling with data; can align stakeholders and drive decisions.