Staff Data Scientist Performance

About the role:

You will act as the technical lead for our Data Science team. The Data Science team sits in our larger Machine Learning organization. The Afresh system comprises, among other sources of data, machine learning forecasts, inventory estimates, user-provided data, and clickstream data. You will work with product managers, go-to-market experts, applied scientists, and other stakeholders to turn this data into metrics and experiments that will guide our company towards more and more food waste reduction.

You will also own a critical part of Afresh’s business: proving the value that we generate for our customers. To do so, you will build on our existing statistical analysis tools to ship rigorous metrics and statistical experiments that demonstrate our impact to our customers and internal teams.

• In your first 3 months, you will familiarize yourself with our existing statistical toolkit and make a scoped improvement to it. You will learn about our ongoing product experiments and metric development, and work with our data scientists to spin up an experiment and/or new metric. You will attend calls with our customers and learn about the nature of data in the grocery industry.
• By the end of your first 6 months, you will introduce additional metrics and ways of slicing at our data that unlock new insights for our customers. You will have onboarded onto our experimentation platform and helped guide our product team towards the right feature choice.
• By the end of your first year, you will make foundational improvements to our causal inference pipelines, making them more scalable and powerful. You will introduce new norms to the team around metric creation and statistical methods.

Skills and experience:

• 4+ years of experience as a data scientist. 6+mo experience as a technical lead where you regularly mentored other team members.
• Expert abilities in statistical analysis, especially in experimental design. Familiarity with causal inference. Extensive experience with both applied and theoretical statistics.
• Expert abilities in SQL. Expert abilities in using Python for data analysis.
• Familiarity with tools for Python statistical analysis (e.g., pandas, statsmodels, sklearn, dplyr).
• Excellent data visualization and dashboarding skills, including familiarity with at least one major dashboarding platform (e.g. Tableau, Looker, Mode).
• Excellent written and verbal visual communication skills with the proven ability to quickly deliver high-quality analyses

 

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