Senior Data Scientist - Customer Analytics & Measurement

Staples • Framingham, MA

2d

About The Position

About the Role Staples is seeking a Senior Data Scientist I with 7+ years of progressively complex experience to lead high-impact work in customer segmentation, personalization, experimentation, and omnichannel measurement, including multi-touch attribution (MTA). This role sits at the intersection of data science, analytics engineering, and applied machine learning, and plays a critical role in shaping how Staples engages customers across digital, in-store, and hybrid (BOPIS / delivery) journeys. You will partner closely with Product, Marketing, Merchandising, and Engineering to drive measurable customer and revenue impact. What You’ll Do Customer Segmentation & Personalization Design and maintain customer segmentation frameworks using large-scale transactional, behavioral, and engagement data. Develop segmentation strategies based on lifecycle stage, purchase frequency, basket composition, category affinity, promotion responsiveness, and channel preference. Build and deploy personalization and targeting models (e.g., propensity, uplift, ranking) to improve engagement, conversion, and retention across marketing and customer touchpoints. Translate analytical and model outputs into actionable decisioning logic. Experimentation & Causal Inference Design, analyze, and interpret experiments and quasi-experiments across marketing, merchandising, and customer engagement use cases. Apply causal inference techniques such as A/B testing, difference-in-differences, matching, uplift modeling, and other incrementality approaches. Support experiments conducted at multiple levels, including customer-, geo-, and store-level designs, while accounting for seasonality, spillover effects, and operational constraints. Partner with stakeholders to ensure tests are well-powered, statistically sound, and aligned with business objectives. Omnichannel Measurement & Attribution Build and evolve omnichannel measurement frameworks, including multi-touch attribution and incrementality models, to assess the impact of customer and marketing touchpoints. Measure the effectiveness of digital and offline channels, such as paid media, email, loyalty programs, promotions, and in-store activity. Clearly communicate model assumptions, limitations, and tradeoffs to technical and non-technical audiences to support decision-making. Data & ML Engineering Collaborate with Analytics and Data Engineering teams to define clean, reliable, and scalable data models at the SKU, transaction, store, and customer level. Productionize analytical models and data products using best practices for code quality, versioning, validation, monitoring, and retraining. Write maintainable, well-documented code and contribute to shared data science tooling and standards. Leadership & Influence Act as a senior individual contributor and technical leader, setting a high bar for analytical rigor and statistical judgment. Review and provide feedback on analyses and models developed by other data scientists. Proactively identify opportunities where data science can improve customer experience, marketing efficiency, and commercial outcomes. Influence strategy with data-driven insights.

Requirements

• 7+ years experience in Data Science, Analytics Engineering, ML Engineering, or related roles.
• Strong foundation in statistics, probability, experimental design, and causal inference.
• Demonstrated experience with customer analytics, including segmentation, personalization, or marketing measurement.
• Hands-on experience designing and analyzing experiments and observational studies in real-world business settings.
• Proficiency in Python and SQL.
• Experience deploying models into production.
• Ability to communicate complex technical concepts clearly to non-technical stakeholders.

Nice To Haves

• Experience in retail, e-commerce, or consumer-facing businesses.
• Experience building or evaluating multi-touch attribution, incrementality, or media measurement models.
• Familiarity with uplift modeling or treatment effect estimation.
• Experience working with modern data stacks (e.g., cloud data warehouses, dbt, feature stores).
• Exposure to ML systems, model monitoring, or MLOps practices.

Responsibilities

• Customer Segmentation & Personalization Design and maintain customer segmentation frameworks using large-scale transactional, behavioral, and engagement data.
• Develop segmentation strategies based on lifecycle stage, purchase frequency, basket composition, category affinity, promotion responsiveness, and channel preference.
• Build and deploy personalization and targeting models (e.g., propensity, uplift, ranking) to improve engagement, conversion, and retention across marketing and customer touchpoints.
• Translate analytical and model outputs into actionable decisioning logic.
• Experimentation & Causal Inference Design, analyze, and interpret experiments and quasi-experiments across marketing, merchandising, and customer engagement use cases.
• Apply causal inference techniques such as A/B testing, difference-in-differences, matching, uplift modeling, and other incrementality approaches.
• Support experiments conducted at multiple levels, including customer-, geo-, and store-level designs, while accounting for seasonality, spillover effects, and operational constraints.
• Partner with stakeholders to ensure tests are well-powered, statistically sound, and aligned with business objectives.
• Omnichannel Measurement & Attribution Build and evolve omnichannel measurement frameworks, including multi-touch attribution and incrementality models, to assess the impact of customer and marketing touchpoints.
• Measure the effectiveness of digital and offline channels, such as paid media, email, loyalty programs, promotions, and in-store activity.
• Clearly communicate model assumptions, limitations, and tradeoffs to technical and non-technical audiences to support decision-making.
• Data & ML Engineering Collaborate with Analytics and Data Engineering teams to define clean, reliable, and scalable data models at the SKU, transaction, store, and customer level.
• Productionize analytical models and data products using best practices for code quality, versioning, validation, monitoring, and retraining.
• Write maintainable, well-documented code and contribute to shared data science tooling and standards.
• Leadership & Influence Act as a senior individual contributor and technical leader, setting a high bar for analytical rigor and statistical judgment.
• Review and provide feedback on analyses and models developed by other data scientists.
• Proactively identify opportunities where data science can improve customer experience, marketing efficiency, and commercial outcomes.
• Influence strategy with data-driven insights.

Benefits

• Generous amount of paid time off and bonus plan.
• 401(k) plan with a company match, medical, dental, vision, life and disability insurance, and many more benefits.
• Associate store discount and more perks (discounts on mobile plans, movie tickets, etc.).
• On-site, discounted childcare, fitness center and dry cleaners in Framingham, MA corporate office.
• The salary range represents the expected compensation for this role at the time of posting. The specific base pay may be influenced by a variety of factors to include the candidate's experience, skill set, education, geography, business considerations, and internal equity.
• In addition to base pay, this role may be eligible for bonuses, or other forms of variable compensation.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed