Data Scientist

Summary

Numerator’s Data Science team provides statistical and methodological leadership across the organization by developing methodologies, tools, and data products that support its products. The Data Scientist will develop and implement applied statistical and data science solutions for consumer purchase panel data, build production workflows and monitoring frameworks, and collaborate with cross-functional partners to improve product quality.

Responsibilities

• Design and implement statistical methodologies that improve the representativeness, stability, and quality of Numerator’s products
• Develop data science solutions to problems involving consumer behavior, panel composition, and data quality using techniques such as sampling, weighting, statistical modeling, classification, predictive modeling, anomaly detection, and optimization
• Build scalable Python and SQL solutions, internal tools, and data products that turn statistical methodologies into reliable production workflows
• Develop metrics, monitoring, and validation frameworks to evaluate system performance and identify unexpected or unintended behavior
• Contribute throughout the project lifecycle, from problem definition and exploration through implementation, testing, deployment, and ongoing evaluation
• Work closely with Product, Engineering, Data Operations, and business partners to shape requirements and develop solutions that are both statistically sound and practical to use
• Communicate methodologies, findings, assumptions, and tradeoffs clearly to technical and non-technical audiences

Skills

• * A bachelor's or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience
• * Two to five years of industry experience developing and delivering data science solutions
• * A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems
• * Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets
• * Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners
• * Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub
• * Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes
• * Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required

Qualifications

Must Haves

• * A bachelor's or advanced degree in Statistics, Mathematics, Data Science, Computer Science, Economics, Physics, or another quantitative field, or equivalent practical experience
• * Two to five years of industry experience developing and delivering data science solutions
• * A strong foundation in applied statistics, with experience applying statistical modeling, optimization, or other quantitative methods to real-world data problems
• * Proficiency in Python and SQL, including experience developing reusable, well-structured code and working with large, complex datasets
• * Comfort working through open-ended problems, investigating unexpected results, and developing solutions with guidance and collaboration, along with the ability to communicate effectively with technical and non-technical partners
• * Experience working with user-level or behavioral data is helpful, as is exposure to production data science workflows and tools such as Snowflake, AWS, Airflow, or GitHub
• * Familiarity with AI-powered tools and workflows, with an interest in applying emerging technologies to improve productivity, learning, and outcomes

Nice to Haves

• * Familiarity with survey research or consumer panels—including sampling, weighting, bias correction, imputation, or other methods used to improve representativeness—is helpful but not required

Benefits

• An inclusive and collaborative company culture
• Volunteer time off
• Charitable donation matching
• Mentorship programs
• Leadership training
• Access to conferences
• Employee resource groups
• Health coverage
• Vision coverage
• Dental coverage
• A Personal Spending Account
• Unlimited PTO
• Flexible scheduling
• 401(k) matching
• Travel reimbursement