Head of Data Science
Numerator | Engineering | Chicago, IL
The ideal candidate is a talented leader who can continue to build out our data science team, technology and process. This is a job for a highly-motivated, considerate, player-coach who will lead our efforts to scale data science to improve our products and operations around the world.
The Head of Data Science owns the tasks related to Data Science product upgrades & support, panel methodology & data training, and data integrity monitoring & the Machine Learning team at Numerator. There will also be client facing projects that involve liaising with the Data Science team.
Along with collaborating with their direct team, data engineers and application developers, the Head of Data Science will weigh in on the architecture, systems and tools to help provide answers that allow Numerator to continue to build out the world's largest single-source set of purchase data across brands and retailers.
- MS in Data Science, Computer Science or similar. PhD is a plus
- 6+ years of hands-on technical experience in a cross-functional data science role
- 2+ years of management experience growing and leading data science teams
- Deep understanding of statistics and experimental design
- Ability to break down complex problems and communicate effectively to move projects forward
- You have production data science experience, i.e. not just fitting a model but being held accountable for performance and reliability in production
- You have strong standards for software engineering practices and can work within a shared codebase
- Manage and mentor a team of data scientists to build amazing products and solve complex engineering and/or business problems.
- Hire and recruit additional data scientists to join your team
Exceptional candidates will have:
- Develop your own KPIs and be able to speak to what metrics really matter to manage a Data Science division well
- Experience configuring and optimizing data pipelines
- Experience in Marketing Analytics field
- Experience in working with academic institutions and other thought leaders to partner on new approaches
- Experience with longitudinal data sets, especially to account for sample stability in modeling
- Experience on AWS Sagemaker stack & pattern
- General software & system design patterns (REST, MVC, Auto-scaling, etc)
- Experience with building internal tools and support structures needed to analyze data, perform elements of data cleaning, feature selection and feature engineering and organize experiments in conjunction with best practices
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by law.