Data Scientist, Corporate

YipitData

35d•Remote

About The Position

YipitData is a leading market research and analytics firm focused on the disruptive economy. The company uses proprietary technology to analyze billions of alternative data points across various sectors to provide actionable insights to clients. YipitData operates globally with offices in the US, APAC, and India, and emphasizes a people-centric culture of transparency, ownership, and continuous mastery. This role is for a Data Scientist to join the core Panel Science team, responsible for building a rigorous single source of truth for enterprise consumer analytics. The position involves working with a sophisticated data engine that combines statistical modeling, data infrastructure, and analytical product architecture. The role is remote-friendly within the US and expects East Coast work hours.

Requirements

4–5 years of professional data science experience (2-3 years with an advanced degree).
Bachelor’s degree in a highly quantitative field (Statistics, Economics, Physics, Mathematics, Engineering, or a related discipline).
Exceptional expertise in working with, modeling, and optimizing for statistical distributions.
Advanced proficiency in Python or R.
Expert-level SQL.

Nice To Haves

Master’s degree is strongly preferred.
Direct experience working with panel data or longitudinal consumer data.
Hands-on experience analyzing receipt data or transaction-level consumer data.
Experience working with Databricks and scaling advanced compute workloads.

Responsibilities

Design, validate, and deploy robust, scalable optimization frameworks and distribution models to ensure complete mathematical integrity across downstream metrics.
Explore and execute complex methodologies, evaluate methodological trade-offs, and implement advanced statistical procedures.
Partner cross-functionally to transition ad hoc code into standardized, version-controlled templates and logical libraries, ensuring analytics are structured cleanly to power diverse product surfaces.
Partner closely with Data Engineering to optimize code performance and scale analytical workflows, transitioning complex statistical calculations into efficient, production-grade cloud compute architectures.