Senior Data Scientist (Reinforcement Learning must have)

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Senior Data Scientist (Reinforcement Learning must have)

Reinforcement Learning (RL) Skill Set

• Understanding of Sequential Decision Making

• RL focuses on agents making a series of decisions to maximize cumulative reward.

• Requires knowledge of Markov Decision Processes (MDPs), policy/value functions, and Bellman equations.

• Algorithmic Expertise in RL

• Familiarity with algorithms like:

• Q-learning, SARSA

• Deep Q-Networks (DQN)

• Policy Gradient methods (REINFORCE, PPO, A3C, DDPG, SAC)

• Experience tuning exploration vs. exploitation strategies.

• Simulation and Environment Design

• Ability to create or work with simulated environments (e.g., OpenAI Gym, Unity ML-Agents).

• Often involves custom reward shaping and environment dynamics modeling.

• Long Training Times and Instability

• RL models are often unstable and require careful tuning.

• Experience with training stability techniques, reward normalization, and experience replay.

• Tooling and Frameworks

• Familiarity with RL-specific libraries like:

• Stable Baselines3

• Ray RLlib

• TensorFlow Agents

• OpenAI Baselines

Team Focus:

• The role is part of a newly formed Global Intelligence Function, supporting cross-business initiatives across three business units: Points, Loyalty, and Hospitality.
• The team is in the early stages of building capabilities and seeks someone who can help shape and grow this function.

Project Scope & Technical Focus:

• Strong emphasis on Reinforcement Learning (RL), especially for dynamic pricing use cases.
• There s currently a skill gap in RL on the Points side, so candidates with RL experience would be especially valuable.
• The approach will be to start simple and evolve iteratively over time.

Role Requirements:

• Ideal candidate is a senior-level, polished professional with a strategic mindset and hands-on experience.
• RL algorithms are open-ended, candidates who can design and iterate based on business needs are highly desirable.
• Strong domain knowledge is required, particularly around rewards, pricing, and loyalty systems.
• Business acumen and the ability to translate technical insights into actionable outcomes are key.

Focus Areas:

• Understanding of RL concepts and practical applications
• Their approach to identifying and defining the "reward" function
• Prior experience implementing RL or similar solutions in a production/business setting
• Ability to align technical efforts with business goals and drive measurable outcomes

Growth Potential:

• The organization is evolving toward a more enterprise-scale operation, offering growth opportunities and cross-functional collaboration.

Ideal Candidate Profile for Roles:

• 5 10+ years of experience in Data Science, Machine Learning, or Applied Statistics
• Strong coding skills in Python; experience with AWS tools (especially SageMaker) is essential
• Able to handle incomplete data or evolving infrastructure (startup mindset preferred)
• Comfortable working independently and engaging cross-functional stakeholders

RL experience is a must have.