Join our team of more than 34,000 team members, supporting our members and communities in our Club Support Center, 235+ clubs and eight distribution centers. BJ’s Wholesale Club offers a collaborative and inclusive environment where all team members can learn, grow and be their authentic selves. Together, we’re committed to providing outstanding service and convenience to our members, helping them save on the products and services they need for their families and homes.

The Benefits Of Working At BJ’s

BJ’s pays weekly
Eligible for free BJ's Inner Circle and Supplemental membership(s)*
Generous time off programs to support busy lifestyles*
Vacation, Personal, Holiday, Sick, Bereavement Leave, Jury Duty
Benefit plans for your changing needs*
Three medical plans**, Health Savings Account (HSA), two dental plans, vision plan, flexible spending
401(k) plan with company match (must be at least 18 years old)
eligibility requirements vary by position
medical plans vary by location

TITLE: Data Scientist

JOB LOCATION: 350 Campus Drive, Marlborough, MA 01752

DUTIES: Extract actionable insights from complex datasets using data mining, statistics, natural language processing, and database techniques to measure/understand/improve member experience.

Apply Data Science methods on a wide range of business applications (Club Operations, Finance, Asset Protection, People Analytics, Marketing, Site Research, etc.).
Work cross functionally with stakeholders to ensure data-drive answers are provided and recommended. Establishes and systematically performs processes to assess and validate data accuracy.
Ensures Data Science models in production are meeting business goals and objectives.
Establishes and systematically performs processes to assess and validate data accuracy.
Ensures Data Science models in production are meeting business goals and objectives.

REQUIREMENTS: Master of Science degree in Data Science, Statistics, or a related field or foreign equivalent, plus 1 year of experience as a Data Scientist or related; 1 year experience Linear Regression, Gradient Boosting, Natural Language Processing, applied data science methods like Clustering and Network Analysis, neural network-based machine learning model, Variable selection, feature engineering, developing and productionizing machine learning models using logistic regression, random forests, gradient boosting using Python and essential libraries such as pandas, NumPy, sklearn, and scipy, extracting data through APIs and analyze complex datasets; automating data ETL and cloud computing environments like Google Cloud, SAS, SQL, analyze unstructured text data, hypothesis testing, application of applied concepts of statistical methods in python, implement algorithms using probability, statistics, linear algebra, building reports and dashboards, creating visualizations; Experience may be gained concurrently.