Lead Machine Learning Engineer

About ZupeeWe are the biggest online gaming company with largest market share in the Indian gaming sector's largest segment — Casual & Boardgame. We make skill-based games that spark joy in the everyday lives of people by engaging, entertaining, and enabling earning while at play. In the three plus years of existence, Zupee has been on a mission to improve people's lives by boosting their learning ability, skills, and cognitive aptitude through scientifically designed gaming experiences.

Zupee presents a timeout from the stressful environments we live in today and sparks joy in the lives of people through its games. Zupee invests in people and bets on creating excellent user experiences to drive phenomenal growth. We have been running profitable at EBT level since Q3, 2020 while closing Series B funding at $102 million, at a valuation of $600 million.

Zupee is all set to transform from a fast-growing startup to a firm contender for the biggest gaming studio in India. .ABOUT THE JOBRole: Lead Machine Learning EngineerReports to: Manager

• Data ScientistLocation: GurgaonJob Summary: We seek a an individual to drive innovation in AI ML-based 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:

• 6 years in machine learning, 4 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 ResponsibilitiesAlgorithm 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 Skills and ExperienceUPLIFT MODELINGBAYESIAN OPTIMIZATIONMULTI-ARMED BANDITSCONTEXTUAL BANDITSPRICING OPTIMIZATIONREINFORCEMENT LEARNING