Chewy is looking for a Senior Applied Economist, Data Scientist or Statistician to join the newly formed Marketing Science function within the Marketing Product, Science and Tech team at Chewy in Boston, MA. The team uses Business Economics, Statistics, and Machine Learning techniques to understand and serve the needs of our pet-parents. Developing data-driven marketing solutions, we are an interdisciplinary team of analysts, who are committed to solving business problems using cutting edge technologies and developing data-driven marketing solutions.
As part of the growing team of strategic analysts and data scientists within Marketing Science, you will have ample opportunities to provide structures to numerous business problems for all things marketing and existing Chewy customers. The problems will range across various stages of the customer lifecycle and marketing levers (product, pricing, promotions, placement, and convenience). With direct exposure to the cross-functional stakeholders, you will have a unique opportunity to formulate analytical solutions for these problems from scratch and develop customer-centric products for efficiency using experimentation, data science and analytics. For such efforts, you will closely work with the data science, analytics, and data engineering teams focused on onsite and offsite marketing.
What You'll Do:
- You will develop the measurement and optimization frameworks to improve the efficiency of the overall decision-making process For that you will closely work the stakeholders within the marketing and merch category teams (Consumables, Hardgoods, Healthcare, etc.), and the data analysts/scientists within the Marketing Analytics team. For example, you will identify the events that attribute to the inflection points in customer behavior and quantify the impact for those events for specific business segments, and the overall business
- You will develop capabilities and automated self-service solutions that will help accelerate the long-term measurement cycles of different business policies, new programs, customers engagement, etc.
- Focusing on customer appetite, willingness to pay and product lifecycle, you will propose state-of-the-art machine learning and experimentation methodologies to maximize the ROI on investments such as promotions, win-back offers, loyalty, retention, etc.
- As you collaborate with the teams to design experiments, measurement, analyses, and recommendations, you will support the outcomes using causal inference methods to bring precision and speed in the decision-making process
- You will develop the capabilities and automated solutions that will help
- quantify the impact of different 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, etc.)
- 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.
- You will be responsible for the full 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, you will work with a team of strategic analysts and engineers to triangulate different inputs and optimize the solutions for different business problems
- You will collaborate with data analysts/scientists and engineers to ideate, architect, and build the technical platforms for our algorithmic engines such as Targeting and Product Optimization for Cross-sell/Up-sell/Autoship/Onboarding, etc., Promotion Optimization, Loyalty Program Optimization, etc.
- You will use data to improve how we make decisions and ultimately, enhance customer experience and drive loyalty. You will surface deep insight hidden in our data lakes and provide tactical and strategic guidance on how to act on findings. It will include developing data-science driven customer segmentation (Lifecycle Segments, Value Segments, etc.)
- You will coordinate with the 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, etc.)
What You'll Need:
- PhD in Economics or highly related field (Finance, Statistics, Math)
- 5+ years of experience in the industry, consulting, government, or academic research
- Experience in applied economic analysis/causal inference and with big data and machine learning techniques
- Experience in building econometric and machine learning models to answer challenging and impactful questions
- Coding ability in a scripting language such as Python, R, Matlab, SQL, SAS or STATA
- Working knowledge of AWS data toolset (Glue, Athena, Sagemaker, Redshift, etc.)
- Experience of crystalizing ambiguous business problems into clear problem definitions and delivering them
- Excellent verbal and written communication skills with the ability to explain details of complex concepts to non-expert stakeholders in a simple understandable way
- Preferably experience of working in customer behavioral science focused on marketing
- Preferably experience of working in the e-commerce industry
- You enjoy working in a team of highly engaged individuals, and you have a passion for data science and KPIs, but also for delivering analysis of the highest quality
- Proven experience in identifying opportunities for business improvement and defining and measuring the success of those initiatives
- Demonstrated ability to dig into the “why” of various results and present data-driven insights and recommendations.
- Proven analytical and quantitative skills and an ability to use data and metrics to back up assumptions, develop business cases, and complete root cause analyses
- Ability to multi-task and work under firm deadlines within a rapidly changing environment
- Passion for data (and fearlessness in the face of a data tsunami) and obsession to use it to drive value for customers and business
- Can work in a fast-paced environment and be persuasive and caring with a wide range of diverse, cross-cultural audiences
- Ability to thrive in complex business environments defined by uncertain, incomplete, or limited information and evolving targets Comfortable finding (or building) common ground to drive his/her agenda
- Strong at operating under constrains, and prioritizing time-sensitive deliveries
- Some travel may be required