REMOTE Lead Data Scientist

New York City, NY | Fully Remote

Job Category: Data Science Job Number: 4294 Annual Salary: $170K Industry: Automotive

Job Description

Job Description:


We have partnered with an established retailer on their search for a Lead Data Scientist. This individual will work with business partners to identify use cases, develop predictive models that solve complex problems, and present findings to senior leadership. You will also oversee the work of more junior data scientists on the team.




• Develop predictive models utilizing large scale data from multiple data sources as well as drive the continuous improvement of existing models.

• Create data-driven solutions for business use-cases utilizing machine learning techniques.

• Identify patterns and insights in the data.

• Identify new data sources and integrate new data into the data stack.

• Interpret and present analytic results to executives.

• Collaborate with key stakeholders to build analytics capabilities and drive business value.

• Assist in technically supervising less experienced Data Scientists.





• Graduate degree in a quantitative discipline.

• 6+ years of data science experience.

• Experience applying statistical techniques across multiple business units or domains (marketing, supply chain, pricing, etc.).

• Strong background in Machine Learning.

• Advanced proficiency with a programming language like Python or R. Python is preferred.

• Ability to work with large data sets from multiple data sources.

• Strong communication skills and proven experience presenting complex analytics concepts and techniques to executives.

• Retail/E-commerce experience.


This is a REMOTE opportunity. Compensation up to $170K.


Keywords: retail, analytics, data science, Machine Learning, Python, ecommerce, predictive modeling, forecasting, pricing, optimization, demand

Meet Your Recruiter

Heidi Kalish
Executive Recruiter

Heidi brings a depth of experience and expertise in recruiting quantitative professionals. She has partnered with candidates within predictive analytics, data science, and operations research and understands the nuances of all of these areas.
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