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.

- Must have 2+ years of hands-on experience as a Data Scientist or Machine Learning Engineer building ML models

- Must have strong expertise in Python with the ability to implement classical ML algorithms including linear regression, logistic regression, decision trees, gradient boosting, etc.

- Must have hands-on experience in minimum 2+ usecaseds out of recommendation systems, image data, fraud/risk detection, price modelling, propensity models

- Must have strong exposure to NLP, including text generation or text classification (Text G), embeddings, similarity models, user profiling, and feature extraction from unstructured text

- Must have experience productionizing ML models through APIs/CI/CD/Docker and working on AWS or GCP environments

- Must be from product companies