Remote Senior ML Scientist (Optimization & Reinforcement Learning)

Description:

• We are seeking an experienced Senior ML Scientist to lead the development of AI/ML-based dynamic pricing algorithms and personalized offer experiences.
• The ideal candidate will specialize in designing and implementing advanced machine learning models, particularly in reinforcement learning techniques such as Contextual Bandits, Q-learning, SARSA, and more.
• By leveraging deep expertise in classical ML and statistical methods, you will create cutting-edge solutions to optimize pricing strategies, improve customer value, and drive measurable business growth.
• Key responsibilities include designing and implementing state-of-the-art ML models for dynamic pricing and personalized recommendations.
• You will develop and apply reinforcement learning techniques to solve pricing and optimization challenges.
• The role involves building AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion rates.
• You will quickly build, test, and iterate on ML prototypes to validate ideas and refine algorithms.
• Developing scalable consumer behavioral feature stores to support ML models is also a key responsibility.
• You will partner with Marketing, Product, and Sales teams to align AI/ML solutions with strategic objectives and deliver measurable outcomes.
• Designing, analyzing, and troubleshooting A/B and multivariate tests to validate model effectiveness is part of the role.

Requirements:

• Candidates must have 8+ years of experience in machine learning, with at least 5 years focusing on reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or AI.
• Expertise in classical ML methods such as Classification, Clustering, and Regression is required, along with familiarity with algorithms like XGBoost, Random Forest, SVM, and KMeans.
• Hands-on experience with reinforcement learning methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization is essential.
• Candidates should be skilled in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.
• Proficiency in Python and SQL, including advanced concepts like Window Functions, Group By, Joins, and Partitioning, is necessary.
• Strong experience with ML frameworks such as sci-kit-learn, TensorFlow, and PyTorch is required.
• Knowledge of causal A/B testing and multivariate testing techniques is essential.

Benefits:

• This position offers a unique opportunity to be at the forefront of AI-driven pricing strategies and personalized offer optimization.
• You will work in a dynamic environment with cross-functional teams, leveraging your expertise to develop impactful machine-learning solutions.
• The role provides the chance to shape the future of AI-driven customer engagement and pricing optimization.