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Data Scientist - Pricing

Posted

J.Crew Group, Inc.
Headquarters: New York, NY
https://jobs.jcrew.com/job/New-York-Data-Scientist-Pricing-NY-10003/420954100
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J.Crew is seeking a Data Scientist with an emphasis on Pricing Analytics to join our team of highly successful predictive analysts. If you have a passion for “wowing” business leaders with exceedingly progressive analytics techniques, working with managers who “get” the work you are doing and support your efforts fully, consistently jumping into assignments focused on innovation rather than “minding the lever” of past projects, working alongside a highly professional and astute team with IT support that’s lightning fast to jump to your aid, then J. Crew is the place for you.

Primary responsibilities include but are not limited to:
Econometric Modeling
Utilizing knowledge of econometrics and time-series modeling to develop department and style-level elasticity and promotion response models to inform business decisions
Developing algorithms to help inform and/or optimize decisions around Initial Ticket Price setting, Promotion planning and execution, and Markdown timing and depth
Implement formal modeling processes from end to end including data gathering, data profiling, numerical model building, calibration, cross-validation, putting product into production, etc.
After building the models, pilot “scorecards” to track model performance and calculated improvement to business – including pre/post time-series analyses, and A/B testing where applicable
Explain complex modeling approaches in layman’s terms and discuss modeling results and business case impacts with non-technical business users

Data Munging
Collect data from a wide variety of corporate databases, including various SQL databases, no-SQL databases, Access databases, Big Data and Excel files
Utilize your toolset in regular expressions to extract information from un-structured text documents
Handle missing data through an algorithmic approach such as multiple imputations to enable insights in sparse and messy data.
Use your inner whiz-kid to feature engineer the data to boost model accuracy

Education & Experience Requirements
Master’s degree preferred in statistics, Economics, Econometrics, Computer Science, Engineering, or Mathematics with a strong quantitative background
At least 5 years previous work experience with strong understanding and proficiency of econometric (including focus on price elasticity) and time-series modeling techniques. Machine learning background a plus
Understanding of modeling techniques and specifically logistic regression, linear regression, cluster analysis, CHAID, market basket analysis, etc.
Superior research, statistical, analytical, processing and mathematical skills with ability to structure and conduct analyses
Proficiency in Microsoft Office Suite, SQL and R and/or Python
Exceptional troubleshooting skills and should thrive in high expectation scenario with many stakeholders
Excellent verbal and written communication skills; ability to present complex information in an easy-to-understand manner with clear recommendations based on data insight
Strongly motivated to be a player in a team which is constantly working to improve themselves through discovering new analytics techniques and software tools to improve the quality of our work

We are committed to affirmatively providing equal opportunity to all associates and qualified applicants without regard to race, color, ancestry, national origin, religion, sex, marital status, age, sexual orientation, gender identity or expression, legally protected physical or mental disability or any other basis protected under applicable law.

J.Crew Group, Inc. is an Equal Employment Opportunity (EEO) Employer
We are committed to affirmatively providing equal opportunity to all associates and qualified applicants without regard to race, color, ancestry, national origin, religion, sex, marital status, age, sexual orientation, gender identity or expression, legally protected physical or mental disability or any other basis protected under applicable law.