Senior ML Scientist (Optimization & Reinforcement Learning)

Job Description

Job Description

Job Title : Senior ML Scientist (Optimization & Reinforcement Learning)

Location : Remote

Expected Start Date : February 17, 2025

Expected End Date : November 17, 2025

Duration : 9 months

Summary

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

Algorithm Development : 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 rates.

Rapid ML Prototyping : Quickly build, test, and iterate on ML prototypes to validate ideas and refine algorithms.

Feature Engineering : Develop scalable consumer behavioral feature stores to support ML models, ensuring high performance and maintainability.

Cross-Functional Collaboration : Partner with Marketing, Product, and Sales teams to align AI / ML solutions with strategic objectives and deliver measurable outcomes.

Controlled Experiments : Design, analyze, and troubleshoot A / B and multivariate tests to validate model effectiveness.

Qualifications

Experience : 8 years in machine learning, with at least 5 years focusing on reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or AI.

ML Techniques : Expertise in classical ML methods (e.g., Classification, Clustering, Regression) and algorithms like XGBoost, Random Forest, SVM, and KMeans.

Reinforcement Learning : Hands-on experience with RL methods such as Contextual Bandits, Q-learning, SARSA, and Bayesian approaches for pricing optimization.

Data Proficiency : Skilled in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.

Programming : Proficient in Python and SQL, including advanced concepts like Window Functions, Group By, Joins, and Partitioning.

ML Frameworks : Strong experience with libraries such as sci-kit-learn, TensorFlow, and PyTorch.

Controlled Experimentation : Knowledge of causal A / B testing and multivariate testing techniques.

Why Join Us?

This is a unique opportunity to be at the forefront of AI-driven pricing strategies and personalized offer optimization. You’ll work in a dynamic environment with cross-functional teams, leveraging your expertise to develop impactful machine-learning solutions.

Apply today and help shape the future of AI-driven customer engagement and pricing optimization!

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