Senior ML Scientist

MDA Edge; Markham; On-site; CAD 80,000 - 100,000; Full time

Job summary

Join a forward-thinking company as a Senior ML Scientist, where your expertise in machine learning and reinforcement learning will drive innovation in AI-based dynamic pricing algorithms. This exciting role involves designing advanced ML models, optimizing pricing strategies, and collaborating with cross-functional teams to enhance customer experiences. As a leader in the field, you'll leverage your skills in classical ML techniques and cutting-edge frameworks to deliver impactful solutions that shape the future of pricing strategies. If you're passionate about AI and eager to make a significant impact, this is the perfect opportunity for you.

Qualifications

• 8+ years in machine learning, with significant experience in reinforcement learning.
• Expertise in classical ML techniques and hands-on experience with RL methods.

Responsibilities

• Conceptualize and implement ML models for dynamic pricing and personalized recommendations.
• Develop RL techniques to optimize pricing strategies and enhance revenue.

Skills

Machine Learning

Reinforcement Learning

Pricing Algorithms

Pattern Recognition

Statistical Methods

Python

SQL

Tools

scikit-learn

TensorFlow

PyTorch

Job description

Job Summary: 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.

Qualifications:

1. 8+ years in machine learning, 5+ years in reinforcement learning, recommendation systems, pricing algorithms, pattern recognition, or artificial intelligence.
2. 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.
3. Proficiency in handling tabular data, including sparsity, cardinality analysis, standardization, and encoding.
4. Proficient in Python and SQL (including Window Functions, Group By, Joins, and Partitioning).
5. Experience with ML frameworks and libraries such as scikit-learn, TensorFlow, and PyTorch.
6. Knowledge of controlled experimentation techniques, including causal A/B testing and multivariate testing.

Key Responsibilities:

1. Algorithm Development: Conceptualize, design, and implement state-of-the-art ML models for dynamic pricing and personalized recommendations.
2. 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.
3. AI Agents for Pricing: Build AI-driven pricing agents that incorporate consumer behavior, demand elasticity, and competitive insights to optimize revenue and conversion.
4. Rapid ML Prototyping: Experience in quickly building, testing, and iterating on ML prototypes to validate ideas and refine algorithms.
5. Feature Engineering: Engineer large-scale consumer behavioral feature stores to support ML models, ensuring scalability and performance.
6. Cross-Functional Collaboration: Work closely with Marketing, Product, and Sales teams to ensure solutions align with strategic objectives and deliver measurable impact.
7. Controlled Experiments: Design, analyze, and troubleshoot A/B and multivariate tests to validate the effectiveness of your models.