**Role:** Senior ML Scientist (Pricing Reinforcement Learning)
**Location:** Mississauga, ON (Remote)
**Type:** Contract

**Job Description:**
We seek a Senior ML Scientist to drive innovation in AI and 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.

**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.

**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 K-Means, 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.