Data Scientist Lead – Measurement and Experimentation
$102,000–$140,000 year
HybridToronto, Ontario, Canada
Job Summary
Provide technical leadership for measurement solutions covering marketing, space productivity, and promotion planning use cases, including A/B testing for space optimization and Bayesian measurement for personalization. Partner with Data Science, Business, and Engineering teams to translate requirements into statistical KPIs and oversee technical aspects of measurement solutions from design to deployment. Lead a team of data scientists to build and manage scalable statistical measurement ETLs in a cloud environment using Databricks, ensuring proper workflow orchestration and automation. Mentor and coach peers through code reviews and pair programming while driving adoption of best practices in Bayesian statistics and Markov chains across the Advanced Analytics team. #LI-Hybrid
Required Qualifications
- Master's or PhD degree in Statistics, Mathematics, Computer Science, or other related technical fields
- 5+ years of industry experience working as a data scientist or similar roles
- at least 2+ years of direct people management or team leadership experience
- Significant experience in end-to-end development and productionization of measurement ETLs and experimentation methodologies in retail, B2C or other similar settings using Python in a cloud environment (e.g., Databricks, Snowflake, Azure Synapse, AWS SageMaker), including model deployment, monitoring, and scalability
- Strong experience with data manipulation using PySpark and SQL
- Proven experience managing multiple stakeholders, concurrent workstreams, priorities and deadlines in a high pressure, fast-paced environment
- Excellent communication skills, with the ability to explain and present complex technical concepts to both technical and non-technical audiences
- Please note: Successful candidates will be required to provide documentation to prove their legal ability to work in the position during the onboarding process
Desired Qualifications
- Familiarity with MLflow, Airflow, and similar MLOps platforms is a plus
- Experience developing and operationalizing Bayesian modelling and experimentation in retail or other B2C setting
- Experience building and deploying Markov Chain models for attribution modeling in retail or other B2C settings
- Experience in building and/or deploying large-scale measurement algorithms in marketing, space planning, promotion planning (e.g., A/B testing or Bayesian simulation) to measure AI/ML applications is highly desirable
- Extensive hands-on experience with statistical modeling techniques using Python – experience using packages such as StatsModel, Numpyro or PyMC is a big plus
- Experience with distributed ML platforms using GPUs and how to optimize them in packages like JAX or TensorFlow or PyTorch is a significant asset
- Strong leadership and mentoring skills, with a proven ability to guide a team of data scientists to achieve ambitious goals and foster a collaborative environment
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