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We are looking for an Analytics Senior Manager, with a data science background, to join our Recommendation Analytics team and lead our default recommendation workstream. This team leverages Wayfairs huge datasets to inform the roadmap of our recommendations. We work in close collaboration with a high performing team of engineers, machine learning engineers/data scientists and product managers who are on the leading edge of the product recommendation space. In this role, you can expect to design, run and analyze the results of A/B tests of new recommendation algorithms and strategies; we also develop new testing and test analysis methodologies (e.g. Bayesian A/B testing). You will do extensive KPI reporting and monitoring (including anomaly detection tools and advanced measurement approaches) and exploratory data analysis (e.g. clustering/segmentation) to generate new insights & inform our product recommendation roadmap. You will use and develop our in-house automated tools; for instance, we have created a QA tool to better understand the performance of our new algorithms before A/B testing them.
What You'll Do
- Communicate key insights and recommendations to cross-functional executive leaders across the organization. You will take complex, nuanced concepts and explain them to stakeholders with differing levels of analytical proficiency.
- Define the strategic analytic agenda for the team, in collaboration with our stakeholders.
- Lead a team of analysts that are responsible for executing the default recommendation analytics agenda.
- Mentor, coach and support your analysts to ensure their success, happiness and well-being at Wayfair.
- Work closely and build productive relationships with our product, engineering and machine learning stakeholders.
- Proactively identify areas where advanced analytical efforts can produce a strong value add and work them into the road map.
What You'll Need
- In-depth experience with SQL (aggregate functions, query optimization, stored procedures, string parsing, Vertica/Hive/Presto/Spark etc) and python & python tools (Jupyter notebooks, functions, pandas, sklearn, matplotlib, etc.)
- In-depth experience with experimental design (A/B tests) and/or statistical analysis to drive business decision making.
- Knowledge and experience using machine learning algorithms/techniques (decision trees, random forest, deep learning, etc.) within a business context. While the data science team is responsible for developing new recommendation algorithms, your team will be testing them and monitoring their performance, so a good understanding of ML is needed.
- Experience (or clear potential) in mentoring and developing a team of analysts.
- Genuine interest in the happiness, well-being, and success of everyone on your team
- Ability to collaborate and proactively communicate with stakeholders
- Strong business acumen, analytical skills and technical abilities along with problem solving skills
- Strong written and verbal communication
Wayfair is one of the worlds largest online destinations for the home. Whether you work in our global headquarters in Boston or Berlin, or in our warehouses or offices throughout the world, were reinventing the way people shop for their homes. Through our commitment to industry-leading technology and creative problem-solving, we are confident that Wayfair will be home to the most rewarding work of your career. If youre looking for rapid growth, constant learning, and dynamic challenges, then youll find that amazing career opportunities are knocking. No matter who you are, Wayfair is a place you can call home. Were a community of innovators, risk-takers, and trailblazers who celebrate our differences, and know that our unique perspectives make us stronger, smarter, and well-positioned for success. We value and rely on the collective voices of our employees, customers, community, and suppliers to help guide us as we build a better Wayfair and world for all. Every voice, every perspective matters. Thats why were proud to be an equal opportunity employer. We do not discriminate on the basis of race, color, ethnicity, ancestry, religion, sex, national origin, sexual orientation, age, citizenship status, marital status, disability, gender identity, gender expression, veteran status, or genetic information.
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