Data Scientist (Product company, Mumbai)

Review Criteria Strong Data Scientist/Machine Learnings/ AI Engineer Profile2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML modelsStrong 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 modelsStrong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured textExperience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environmentsPreferred (Company) Must be from product companies Job Specific Criteria CV Attachment is mandatoryWhat'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 personalizationBoost revenue via pricing/discount optimizationPower user-to-user discovery and ranking (matchmaking at scale)Detect and block fraud/risk in real timeScore conversion/churn/acceptance propensity for targeted actionsCollaborate 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 25 years of DS/ML experience in consumer internet / B2C products, with 78 models shipped to production end-to-end.Proven, hands-on success in at least two (preferably 34) 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. Review Criteria Strong Data Scientist/Machine Learnings/ AI Engineer Profile2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML modelsStrong 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 modelsStrong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured textExperience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environmentsPreferred (Company) Must be from product companies Job Specific Criteria CV Attachment is mandatoryWhat'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 personalizationBoost revenue via pricing/discount optimizationPower user-to-user discovery and ranking (matchmaking at scale)Detect and block fraud/risk in real timeScore conversion/churn/acceptance propensity for targeted actionsCollaborate 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 25 years of DS/ML experience in consumer internet / B2C products, with 78 models shipped to production end-to-end.Proven, hands-on success in at least two (preferably 34) 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; fa