Numerator is a data and technology company reinventing market research. Headquartered in Chicago, IL, Numerator has over 1,600 employees worldwide. The company blends proprietary data with advanced technology to create unique insights for the market research industry. The majority of Fortune 100 companies are Numerator clients. What are you waiting for, join us on this amazing journey! Numerator is looking for a Data Science Analyst to join our growing Data Business Operations team. This is a unique opportunity where you will get a chance to work with an established and rapidly evolving platform that handles millions of requests and massive amounts of events, and other data. In this position, you will be responsible for taking on new initiatives to automate, enhance, maintain, and scale services in a rapidly-scaling environment. This Data Science Analyst position is a data quality oriented role that creates value and drives impact across the Product organization. The role is cross-functional by nature and is responsible for investigating pressing data issues surfaced by both internal and external clients and identifying the root cause so that we can collaboratively move towards a solution. The ideal candidate has analytical experience, with an understanding of data transformations and data queries. Responsibilities: Forensic Data Analysis Isolate underlying root causes of data integrity issues across product and platform Communicate technical findings to our non-technical consultants in an approachable, understandable manner Coordinate with engineers and technical team members to resolve bugs and creatively resolve root causes Work closely with panel science team to coordinate product-side fixes arising from data science design Internal Product Development Partner with Product and Engineering team members on complex data projects Identify commonalities among root cause and develop efficiency-building products to streamline forensic data analysis to isolate bugs more quickly Present developed products to first and second tier stakeholders Transfer ownership of complex projects from data scientists and handoff built projects to more junior team members