Data Scientist, Modeling and Experimentation

Number of Role Openings: 2

We are seeking a results-oriented senior data science professional who can support retail, loyalty, and customer analytics. The primary focus of this position is to design and deliver analytical measurement frameworks for AB experiments, perform advanced statistical models, build causal inference frameworks, and perform customer and program analytics. The successful candidate will perform analytics in support of customer engagement, loyalty program optimization, and marketing program efficiency. Experiments, models, analytics, and insights developed will help achieve the strategic goals of our Retail organization. Strong background in statistics, causal models and experimental design preferred.

What you’ll do

• Develop a deep understanding of our Retail business, Loyalty, Store Operations, and Marketing programs.

• Build comprehensive market, store and user-level test designs with explicit success criteria

• Research statistical methodology to aid in protocol development (study design and sample size calculation) and analyses planning

• Collaborate in cross-functional teams to generate insights and test measurement frameworks that improve business practices and generate measurable business value.

• Construct and improve AB test design and predictive models to reduce test costs and improve result confidence

• Validate, analyze and investigate test data from multiple perspectives to weave compelling fact-based narratives

• Develop statistical, causal, and machine learning models to improve customer engagement and marketing program performance.

• Act as an active contributor to the analytics strategy within the Advanced Analytics team.

Key Relationships

• Peers within the Advanced Analytics and Modelling teams

• Business Stakeholders throughout the retail organization.

What you bring

• M.S or PhD, preferably in Statistics/Math/Economics or a related quantitative discipline.

• 3+ years experience developing and implementing data science solutions. Experience in retail businesses, or loyalty programs preferred.

• Proficiency in using industry standard programming languages, including Python, R, or SAS.

• 3+ years of experience querying and analyzing large datasets with tools such as SQL, Spark, etc.

• Demonstrated understanding of statistical techniques to programmatically select (and justify) the right approach given specific business problems and data/technology constraints.

• Familiarity with cloud-based data structures, including tools to extract, manipulate and transform data within the cloud. Experience with Databricks and Azure Synapse is an asset.

• 3+ years interpreting, synthesizing, and presenting complex models and analyses to business partners.

• Excellent oral and written communication skills, with the ability to communicate both technical and business concepts, as well as strong presentation skills.

• Demonstrated ability to work independently with minimal supervision, effectively navigating and resolving ambiguous problems and situations

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 and $64,000.00 and $106,000.000 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.

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.