Marketing Data Scientist

Marketing Data Scientist – BMO

We are seeking a highly skilled and inquisitive Marketing Data Scientist to join our North American Marketing Data Science team. This role will be pivotal in advancing our in‑house capabilities in Marketing Mix Modeling (MMM), experimentation, and multi‑touch attribution (MTA). The ideal candidate will have hands‑on experience with open‑source MMM tools, causal inference, and A/B testing frameworks, and will be passionate about translating complex data into actionable insights that drive smarter media investment, customer engagement, and marketing optimization.

• Consult on analytical solutions to understand, analyze, and synthesize business requirements, enabling high‑quality, fact‑based decisions that drive better marketing outcomes and support strategic initiatives.
• Lead the development and implementation of in‑house MMM solutions using open‑source packages (e.g., Robyn, Meridian, Lightweight
  
  MMM, PyMC).
• Design, develop, and implement innovative analytical solutions including MMM, MTA, and causal inference frameworks.
• Design and execute robust experimentation frameworks including A/B testing, uplift modeling, and causal inference to evaluate marketing effectiveness.
• Provide expert guidance on the configuration, functionality, and usability of data management, analytics, and visualization technologies.
• Support the development of strategy and roadmap for data quality, modeling, reporting, and advanced decision support tools and develop scalable, reproducible analytical pipelines for marketing analytics.
• Build and maintain effective relationships with internal and external stakeholders to align analytics with business goals.
• Structure and assemble multi‑dimensional data sets across various granularities (e.g., customer, product, transaction, media).
• Integrate data from multiple sources to enhance analysis, streamline reporting, and improve marketing performance measurement.
• Document and maintain operational procedures related to analytical and reporting processes.

• Typically 4–6 years of experience in marketing analytics, data science, or econometrics, with a post‑secondary degree in a quantitative field (e.g., Statistics, Economics, Data Science, Computer Science, Marketing Analytics).

• Strong hands‑on experience with open‑source MMM tools (e.g., Robyn, Lightweight
  
  MMM, PyMC, Prophet).
• Proficiency in Python and SQL; familiarity with R is a plus.
• Solid understanding of MTA, attribution modeling, causal inference, and design of experimentation.
• Experience with cloud‑based data platforms (e.g., AWS, GCP, Data Iku).
• Skilled in data visualization tools (e.g., Power
  
  BI, Looker, Tableau).

• Strong analytical and problem‑solving skills with a data‑driven mindset.
• Excellent communication skills with the ability to influence and collaborate across teams.
• Ability to work independently on complex analytical tasks and deliver high‑impact insights.

• Familiarity with traditional and digital media data sources (e.g., Kantar, Nielsen, Google Ads, Meta).
• Experience building custom MMM models from scratch and validating them through experimentation.
• Knowledge of Bayesian modeling and probabilistic programming.

$67,200.00 – $

Salaried

BMO also offers health insurance, tuition reimbursement, accident and life insurance, and retirement savings plans. Total compensation will vary and may include performance‑based incentives, discretionary bonuses, and other perks. For full details see:

At BMO we are driven by a shared

Purpose:

Boldly Grow the Good in business and life. We create lasting, positive change for our customers, communities, and people. We support growth from day one with training, coaching, mentorship, and networking opportunities.

Seniority level: Not Applicable

Employment type: Full‑time

Job function: Engineering and Information Technology

BMO is committed to an inclusive, equitable, and accessible workplace. Accommodations are available on request for candidates taking part in all aspects of the selection process.