Our Opportunity:

We are looking for a Data Scientist II at our facility in Boston, Massachusetts within the Marketing Science team to develop the measurement and optimization frameworks to improve the efficiency of the overall decision-making process.

What You’ll Do: 

• Closely work with the marketing and merch category stakeholders (Consumables, hard goods, Healthcare) and the data analysts/scientists within the Marketing Analytics team.
• Identify the events that contribute to the inflection points in customer behavior and quantify the impact of those events for specific business segments and the overall business.
• Develop capabilities and automated self-service solutions that will help accelerate the long-term measurement cycles of different business policies, new programs, and customer engagement.
• Focusing on customer appetite, willingness to pay, and product lifecycle, propose state-of-the-art machine learning and experimentation methodologies to maximize the ROI on investments such as promotions, win-back offers, loyalty, and retention.
• Collaborate with the teams to design experiments, measurements, analyses, and recommendations, supporting the outcomes using causal inference methods to bring precision and speed to the decision-making process.
• Develop capabilities and automated solutions.
• Quantify the impact of strategic decisions, customer-driven actions, and business processes. Use data-science approaches to bridge changes in KPIs to internal drivers (ex, customer/product/channel mix).
• Push the tenet to continue to focus on incrementality.
• Provide data-driven guidance on key business decisions and trade-offs.
• Audit the projected downstream benefits against the realized benefits and adjust future projections accordingly.
• Responsible for the entire Data Science lifecycle from conception to prototyping, testing, deploying, and measuring the overall business value of the models.
• Also, you will periodically develop the model health reports to ensure the integrity of the underlying processes and assumptions.
• If needed, you will refit the model following the model lifecycle.
• Using the ML model outputs, work with a team of strategic analysts and engineers to triangulate different inputs and optimize the solutions for different business problems.
• Collaborate with data analysts/scientists and engineers to ideate and architect large scale ML models for Targeting and Product Optimization for Cross-sell/Upsell/Autoship/Onboarding, Promotion Optimization, and Loyalty Program Optimization.
• Use data to improve our decisions and, ultimately, enhance customer experience and drive loyalty.
• Surface deep insight hidden in our data lakes and provide tactical and strategic guidance on acting on findings. It will include developing data-science-driven customer segmentation (Lifecycle Segments and Value Segments).
• Coordinate with data engineering teams to develop the automated pipelines to perform different stages of the model life cycle (data collection and cleaning, model development and validation, model deployment and scoring, periodic validation, and refitting).
• Local telecommuting permitted up to 3 days a week.

What You’ll Need:

• Master’s degree in Science, Economics, Statistics, Mathematics, Engineering, or a related field and 1 year of experience required.
• Experience must include 1 year with the following: applied economic analysis/causal inference and with big data and machine learning techniques;
• Building econometric and machine learning models to answer challenging and impactful questions;
• Demonstrated coding ability in a scripting language like Python, R, or equivalent;
• Working knowledge of AWS data toolset;
• Identifying opportunities for business improvement and defining and measuring the success of those initiatives;
• Analytical and quantitative skills;
• Application of data and metrics to back up assumptions, develop business cases, and complete root cause analyses;
• SQL language, the Snowflake and Vertica data technologies;
• Statistical methods of regressions and forecasting; and
• AI/ML toolset, such as Outerbounds.
• Local telecommuting permitted up to 3 days a week.
• The position is eligible for the Employee Referral Program.