Senior ML Scientist (Optimization & Reinforcement Learning)

📋 Description

• Design and deploy ML/RL models for pricing, personalization, and recommendations at scale.
• Apply reinforcement learning techniques: Contextual Bandits, Q-learning, SARSA, Bayesian
• Develop AI-powered pricing agents leveraging consumer behavior and competitive signals.
• Prototype, test, and iterate ML solutions quickly to validate hypotheses and refine algorithms.
• Build large-scale feature stores and engineer consumer behavioral features.
• Design controlled experiments (causal A/B, multivariate) to evaluate impact.

🎯 Requirements

• Experience: 8+ years in ML; 5+ years in RL, recommendation systems, pricing algorithms, or related
• ML Expertise: classical ML methods with XGBoost, Random Forest, SVM, KMeans.
• RL: Expertise with Contextual Bandits, Q-learning, SARSA, Bayesian approaches, Thompson Sampling
• Data: Strong with tabular data, encoding, feature engineering.
• Programming: Python and SQL, including window functions, GROUP BY, JOINs, partitioning.
• ML Frameworks: scikit-learn, TensorFlow, PyTorch.

🎁 Benefits

• Work on advanced ML, RL, optimization, dynamic pricing, personalization.
• High-impact role influencing business outcomes via AI.
• Exposure to large-scale consumer data and real-world ML apps.
• Collaborative environment with cross-functional teams.
• Flexible working arrangements per partner company policies.
• Competitive compensation based on experience and market alignment.