About the role
- Audience Data Analysis: Analyze audience files, internal crosswalks, and client/vendor datasets to identify fragmented connections, anomalous records, and incorrect person or household associations.
- Data Quality & Match Assessment: Investigate and quantify false positives, false negatives, and other data matching quality indicators.
- Validation & Testing: Validate audience outputs, matching behavior, and data connectivity across real-world business scenarios and use cases.
- SQL & Python Analytics: Use SQL and Python or R to query, process, profile, and analyze large and complex datasets.
- Benchmarking & Test Data: Create, maintain, and enhance datasets for testing, validation, benchmarking, and regression analysis.
- Exploratory Data Analysis: Perform detailed and ad hoc analyses to uncover patterns, anomalies, trends, and opportunities for improving audience data quality.
- Audience Optimization: Partner with Product and Engineering teams to improve matching accuracy, audience connectivity, data coverage, and overall product performance.
- Statistical Analysis: Apply appropriate analytical methods and quality metrics to evaluate audience matching effectiveness and data reliability.
- Reporting & Insights: Prepare reports and document findings, recommendations, quality trends, and performance insights for technical and business stakeholders.
- Cross-Functional Collaboration: Support the Lead Data Scientist and collaborate with Product, Engineering, and Data Science teams on validation, testing, and research initiatives.
- Continuous Improvement: Contribute to testing backlogs, validation workflows, quality monitoring, and measurement processes to improve audience product adoption and reliability.
- Bachelor's degree in Analytics, Data Science, Statistics, Mathematics, Computer Science, or a related field.
- 4+ years of experience in data analytics, data quality, data validation, or a related analytical role.
- Strong proficiency in SQL, including complex queries, joins, aggregations, data profiling, and analysis of large datasets.
- Hands-on experience with Python or R for data analysis, data manipulation, and statistical evaluation.
- Strong understanding of data quality assessment, validation methodologies, and exploratory data analysis.
- Experience identifying anomalies, duplicates, inconsistencies, and patterns across complex datasets.
- Knowledge of statistical analysis and data-driven problem-solving techniques.
- Strong analytical thinking, attention to detail, and the ability to translate findings into actionable recommendations.
- Excellent communication skills and the ability to collaborate with cross-functional technical and business teams.
- Experience in audience analytics, audience generation, identity resolution, entity matching, or customer identity data.
- Familiarity with audience data products, data crosswalks, identity mapping, and person-level or household-level data relationships.
- Experience working with large-scale, data-intensive platforms and complex client/vendor datasets.
- Understanding of match-quality measurement, including false-positive and false-negative analysis.
- Experience creating benchmarking datasets, test scenarios, and regression testing workflows.
- Exposure to Customer Data Platforms (CDPs), MarTech, AdTech, audience intelligence, or digital marketing ecosystems.
- Familiarity with cloud-based data platforms such as Snowflake, Databricks, or equivalent technologies.
- You enjoy investigating complex datasets and identifying meaningful patterns and data quality issues.
- You can use SQL, Python, and statistical techniques to validate data and measure product effectiveness.
- You have a structured approach to problem-solving and can convert analytical findings into measurable improvements.
- You work effectively with cross-functional teams to enhance data accuracy, connectivity, and product performance.