Data Scientist-II (Traditional/Classic ML, Neural networks exp must)

What You’ll Do

• 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

What We’re Looking For

• 2–4 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.

Skills:- neural networks, Deep Learning, XGBoost, Linear regression, Long short-term memory (LSTM), CNN, KNN, Recurrent neural network (RNN), Machine Learning (ML) and catboost