Manager, Statistical Modelling & Advanced Analytics

Reporting to the AVP, Advanced Analytics and Modelling, the Manager, Statistical Modelling & Advanced Analytics will lead high-impact statistical modelling work that helps shape decisions across retail, customer, and loyalty. This role is ideal for someone who enjoys solving complex business problems with rigorous quantitative methods and translating that work into real business action.

You will have the chance to directly impact customer growth and retention, promotional effectiveness, and strategic retail decision-making. You’ll have the chance to work with large, complex datasets and influence decisions that matter across the enterprise.

What you’ll do

• Lead the design and development of advanced statistical and quantitative models using retail, customer, and loyalty data

• Apply methods such as forecasting, causal inference, experimentation, predictive modelling, segmentation, optimization, and statistical learning to solve important business problems

• Partner closely with business leaders to identify where modelling can most improve decision-making, customer outcomes, and financial performance

• Build models and analytical frameworks that are practical, scalable, and decision-oriented

• Establish strong standards for model design, validation, monitoring, and measurement

• Translate complex quantitative work into clear recommendations for senior leaders

• Help shape the future of modelling and experimentation within the broader analytics organization

• Coach and develop a team of analysts and data scientists, while remaining close to the work and setting a high bar for technical quality

What you bring

• Graduate degree in Statistics, Mathematics, Econometrics, Economics, Data Science, Computer Science, or another quantitative field

• PhD is welcome, but not required

• Significant experience in statistical modelling, advanced analytics, or quantitative decision support

• Strong command of methods such as:

  Regression and classification

  Forecasting / time series

  Experimental design and measurement

  Causal inference

  Statistical learning

  Model validation and performance assessment

• Strong coding experience in Python

• Experience working with large datasets using SQL, Spark, or similar tools

• Experience in modern data environments such as Databricks, cloud platforms, or distributed computing frameworks

• Strong communication skills, with the ability to explain technical work clearly to non-technical audiences

• Experience leading projects, influencing senior stakeholders, and helping others raise the quality of their work

• In addition to the above qualifications, preference will be given to candidates who have:

  Experience in retail, loyalty, consumer, pricing, marketing, or customer analytics

  Experience with experimentation platforms, uplift modelling, optimization, or decision science

  Experience building modelling workflows that move beyond analysis into operational use

We’re always looking for great talent! In addition to competitive pay, we offer:

• Comprehensive benefits and retirement programs

• Performance incentives, Continuing Education Programs

• Other perks to support your well-being

• Career growth opportunities and product discounts

Our typical hiring range is between $80,000.00 and $140,000.00 per annum. Salary decisions are also dependent on other factors such as your experience, job-related knowledge, skills and competencies, market location, industry benchmarks, internal equity and other role-specific requirements. We're committed to attracting top talent. For critical roles, the compensation offering will be reviewed to ensure alignment with market rate and conditions and the unique value you bring to the role. #LI-AG2

This posting represents an existing vacancy within our organization.

We may use artificial intelligence tools as part of our recruitment process to assist in the initial screening of resumes. All hiring decisions, including candidate evaluation, selection, and disposition, are made by human recruiters.