Sr Manager Of Data Science Analytics at Everlane

Everlane is seeking a Sr. Manager/Director to join our team, where you’ll help drive growth of the Everlane business by leading a team that develops predictive models, collaborates with business partners to drive decisions, and builds the next generation of Everlane’s data infrastructure that underlies our models and reports. The subject areas that you will impact span the entire company, from helping our digital product team evaluate new opportunities of improvement on our website, to building a model around the purchase preferences of our customers.
Our team is tasked with extracting actionable insights from our data, building predictive data products, and creating self-service analytical tools for teams throughout the company.  We use a full-stack set of methodologies and tools to achieve this, taking a data science problem from the initial open ended business question, to the eventual productionisation and implementation of a model.
Your day-to-day:

Own the Data architecture and roadmap, working independently to understand, prioritize, and plan the team’s work
Effectively communicate and drive collaboration with key stakeholders across the business
Hire and oversee data analysts, data scientists, and data engineers
Be a source of technical leadership, helping coach direct reports and developing their skills
Research and promote opportunities to take on new technologies, frameworks, and approaches in AB testing, Data Pipelining, and Machine Learning
Apply causal research and explanatory modeling to better understand customer behavior, marketing performance etc.
Deploy predictive data products that our customers will interact with, including product recommendations, size predictions, and predicted return rates
Design and analyze rigorous experiments aimed at understanding customer behavior
Scale the Everlane data platform with smarter and more sophisticated data pipelines

We'd love to hear from you if you have:

BA/BS degree in a quantitative or computing focused degree
7+ years of full stack experience (data collection, analysis, machine learning, statistical modeling)
3+ years of people management experience
Ability to understand and interpret the business from both a stakeholder and data point of view
Expertise in R, Python, or another data science suitable language
Expertise in SQL
Strong fundamentals in statistics and probability, including formal statistical modeling and experimental design

Nice to have: 



Experience in retail/e-commerce or a B2C technology company
Experience working on data problems in a distributed framework, i.e. Spark, Hadoop





Remote Positions: This is an optional remote position. Please note: We are only accepting applications from those who file their taxes in one of the following states: California, Colorado, Florida, Illinois, Kansas, Massachusetts, Minnesota, North Carolina, New Hampshire, New Jersey, New York, Ohio, Oregon, Pennsylvania, South Carolina, Tennessee, Texas, Utah, Virginia, and Washington.
If you are a Colorado resident, this is for you: At Everlane, we carefully consider a wide range of compensation factors, including your background and experience. These considerations can cause your compensation to vary. The compensation for this role starts at a minimum annual salary $137,000. The actual pay may be higher depending on your skills, qualifications, and experience. 
If you are a New York resident, this is for you: At Everlane, we carefully consider a wide range of compensation factors, including your background and experience. These considerations can cause your compensation to vary. The compensation for this role starts at a minimum annual salary $152,000. The actual pay may be higher depending on your skills, qualifications, and experience. 
As part of our commitment to health and safety for our teams, we’re taking a responsible approach to creating the safest working environment we can. As part of that commitment, COVID-19 vaccines are required for all current and future Everlane Team Members that do not require an exemption.