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Senior ML Scientist (Optimization & Reinforcement Learning) Job Opening In San Jose California – Now Hiring Macpower Digital Assets Edge Private Limited


Job description

Job Summary: We seek a Senior ML Scientist to drive innovation in AI ML-based dynamic pricing algorithms and personalized offer experiences.

This role will focus on designing and implementing advanced machine learning models, including reinforcement learning techniques like Contextual Bandits, Q-learning, SARSA, and more.

By leveraging algorithmic expertise in classical ML and statistical methods, you will develop solutions that optimize pricing strategies, improve customer value, and drive measurable business impact.

Qualifications:


  • 8+ years in machine learning, 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence.


  • Expertise in classical ML techniques (e.g., Classification, Clustering, Regression) using algorithms like XGBoost, Random Forest, SVM, and KMeans, with hands-on experience in RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization.


  • Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.





  • Proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning).


  • Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch




  • Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing.




Key Responsibilities:


  • Algorithm Development: Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations.


  • Reinforcement Learning Expertise: Develop and apply RL techniques, including Contextual Bandits, Q-learning, SARSA, and concepts like Thompson Sampling and Bayesian Optimization, to solve pricing and optimization challenges.


  • AI Agents for Pricing: Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion.


  • Rapid ML Prototyping: Experience in quickly building, testing, and iterating on ML prototypes to validate ideas and refine algorithms.


  • Feature Engineering: Engineer large-scale consumer behavioral feature stores to support ML models, ensuring scalability and performance.


  • Cross-Functional Collaboration: Work closely with Marketing, Product, and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact.


  • Controlled Experiments: Design, analyze, and troubleshoot A/B and multivariate tests to validate the effectiveness of your models.



Required Skill Profession

Other General


  • Job Details

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