Senior ML Scientist (Optimization & Reinforcement Learning)

Syndesus; Toronto; On-site; CAD 120,000 - 150,000; Full time

Job summary

A leading company in AI innovation is seeking a Senior ML Scientist to enhance pricing strategies through advanced machine learning models. The role involves developing reinforcement learning techniques, collaborating with various teams, and delivering significant business impact through innovative solutions.

Qualifications

• 8+ years in machine learning and 5+ years in reinforcement learning.
• Expertise in modern ML techniques and reinforcement learning methods.
• Proficient in data handling and model prototyping.

Responsibilities

• Develop and implement ML models for dynamic pricing.
• Build AI-driven pricing agents using consumer behaviour insights.
• Collaborate with cross-functional teams to align solutions with objectives.

Skills

Machine Learning

Reinforcement Learning

Recommendation Systems

Data Analysis

Tools

Python

SQL

Job description

Role Overview

We seek a Senior ML Scientist to drive innovation in AI 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 behaviour, 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 behavioural 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 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).