5+ years of experience in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence.
Proficiency in Python and SQL (including window functions, grouping, joins, and partitioning) with hands-on experience in ML frameworks such as scikit-learn, TensorFlow, and PyTorch.
Expertise in classical ML techniques (classification, regression, clustering) using algorithms like XGBoost, Random Forest, SVM, and KMeans; RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization.
Requirements:
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 (Contextual Bandits, Q-learning, SARSA; Thompson Sampling; Bayesian Optimization) to 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.
Experimentation and Prototyping: Design, run, and analyze controlled experiments (A/B and multivariate tests) and rapidly prototype ML solutions to validate ideas and refine algorithms.
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.
