Senior ML Scientist (Optimization & Reinforcement Learning)

Accountabilities:

• • Algorithm Development: Conceptualize, design, implement, and optimize advanced machine learning models for dynamic pricing, personalization, and recommendation use cases.
  • Reinforcement Learning: Apply techniques such as Contextual Bandits, Q-learning, SARSA, Thompson Sampling, and Bayesian Optimization to solve complex pricing and optimization problems.
  • AI-Powered Pricing Agents: Develop intelligent pricing agents that incorporate consumer behavior, demand elasticity, competitive signals, and other relevant factors to optimize revenue and conversion.
  • Rapid Prototyping: Quickly develop, test, and iterate on machine learning prototypes to validate hypotheses, assess feasibility, and refine algorithms.
  • Feature Engineering: Build and optimize large-scale consumer behavioral feature sets and feature stores that support scalable, high-performance machine learning applications.
  • Experimentation: Design, analyze, and troubleshoot controlled experiments, including causal A/B and multivariate testing, to evaluate model effectiveness and business impact.
  • Cross-Functional Collaboration: Partner with Product, Marketing, Sales, and other stakeholders to translate business objectives into effective ML solutions and measurable outcomes.

  Requirements

  • Experience: 8+ years of experience in machine learning, with 5+ years of hands-on experience in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, artificial intelligence, or closely related fields.
  • Machine Learning Expertise: Strong knowledge of classical ML methods, including classification, clustering, and regression, with practical experience using algorithms such as XGBoost, Random Forest, SVM, and KMeans.
  • Reinforcement Learning: Demonstrated expertise with Contextual Bandits, Q-learning, SARSA, Bayesian approaches, Thompson Sampling, Bayesian Optimization, and related optimization techniques.
  • Data Expertise: Strong experience working with tabular data, including sparse datasets, cardinality analysis, standardization, encoding, and feature engineering.
  • Programming: Proficiency in Python and SQL, including Window Functions, GROUP BY, JOINs, and partitioning.
  • ML Frameworks: Hands-on experience with machine learning libraries and frameworks such as scikit-learn, TensorFlow, and PyTorch.
  • Experimentation: Knowledge of controlled experimentation methodologies, including causal A/B testing and multivariate testing.
  • Problem-Solving: Strong analytical and quantitative skills, with the ability to translate complex optimization and ML challenges into practical, scalable solutions.
  • Collaboration: Excellent communication skills and the ability to work effectively with technical and non-technical stakeholders across multiple functions.

  Benefits

  • Opportunity to work on advanced machine learning, reinforcement learning, optimization, dynamic pricing, and personalization challenges.
  • High-impact role with the opportunity to influence measurable business outcomes through AI-driven solutions.
  • Exposure to large-scale consumer data, experimentation, and real-world ML applications.
  • Collaborative environment with cross-functional interaction across Product, Marketing, Sales, and technical teams.
  • Flexibility and working arrangements aligned with the partner company's policies and role requirements.
  • Competitive compensation package based on experience, skills, and market alignment.
  • Access to benefits and employee programs provided under the partner company's applicable employment policies.