ML Scientist (Pricing Reinforcement Learning) | REMOTE

Senior ML Scientist

100% telecommute

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.

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 behaviour, 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 behavioural 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 AB and multivariate tests to validate the effectiveness of your models

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 eg 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, Tensor Flow and Py Torch
• Knowledge of controlled experimentation techniques including causal AB testing and multivariate testing
• 5+ Yrs Experience in Pricing Reinforcement Learning
• 8+ Yrs Experience in Machine Learning
• Expert in Python & Tabular Data
• SQL
• Knowledge of AB Testing