Machine Learning Engineer

Machine Learning Engineer – Recommendation Systems / Propensity ModelingRole OverviewWe are looking for a Machine Learning Engineer / Data Scientist to design and build a high-scale offer recommendation system that personalizes and ranks offers for millions of users to improve engagement and conversion. The role focuses on propensity modeling, ranking systems, and personalization in a dynamic user environment with limited and noisy data.Role SummaryML Engineer who can build a scalable offer recommendation system using propensity modeling and ranking techniques to improve user engagement in a dynamic, sparse-data environment.Business Context· Platform displays multiple third-party offers (10–20 offers per user)· Goal: Show top 1–3 offers and maximize CTR, conversion, and engagement· Scale: Millions of users with dynamic and short-lived user base· Constraints: Limited user history, cold start, privacy constraints (no third-party data), sparse dataKey ResponsibilitiesRecommendation System Design:· Design end-to-end offer recommendation pipeline· Build personalized ranking systems for user-offer matchingPropensity Modeling & Ranking:· Predict CTR and engagement probability· Use models like XGBoost / LightGBM· Optimize ranking using NDCG, MRR, Precision@KFeature Engineering:· Build features from user behavior, interaction signals, and context· Handle sparse and noisy dataCold Start Handling:· Design strategies for new users and new offers· Implement hybrid and fallback approachesBias Handling:· Mitigate popularity and exposure bias· Implement diversity and re-ranking strategiesModel Evaluation:· Define and track CTR, conversion, NDCG, AUC· Continuously improve engagement metricsScalability & Deployment:· Build systems for millions of users· Enable real-time or near real-time inferenceRequired Skills· Strong experience in ML: classification, regression, propensity modeling· Experience with recommendation systems and ranking models· Hands-on with XGBoost, LightGBM· Python, SQL, feature engineering· Experience with MLOps and model deploymentNice to Have· Experience with AWS SageMaker or Databricks· Experience with LLM-based recommendation approaches· A/B testing and experimentation knowledge