Job Role: Data Scientist

POSITION OVERVIEW

DUTIES AND RESPONSIBILITIES

Primary Responsibilities: Data Science and Measurement Analytics

• Support the design, development, testing, and validation of statistical and machine learning models for business measurement and analytical use cases.
• Perform exploratory data analysis (EDA), data preprocessing, feature engineering, and statistical analysis on large, complex datasets.
• Apply statistical modeling, hypothesis testing, experimental design, and causal inference techniques to identify patterns, measure outcomes, and generate actionable insights.
• Develop and evaluate models supporting incrementality and lift analysis, multi-touch attribution (MTA), marketing mix modeling (MMM), and rules-based attribution, where applicable.
• Analyze data from multiple sources, including customer interactions, media exposure, commerce, engagement, and business outcomes.
• Collaborate with Data Engineering teams to validate data quality, feature pipelines, data transformations, and model input datasets.
• Support the integration and validation of new data sources and partner feeds into analytical and modeling workflows.
• Evaluate model performance using appropriate statistical metrics, validation techniques, and performance benchmarks.
• Monitor deployed models and analytical workflows, identify data or performance issues, and recommend improvements.
• Document analytical methodologies, modeling assumptions, experiments, results, and reusable best practices.
• Translate business requirements into analytical problems and communicate findings, insights, and recommendations to technical and non-technical stakeholders.

Secondary Responsibilities: Identity Analytics and Privacy-Aware Data Science

• Support privacy-conscious analytical approaches that generate meaningful aggregate insights while protecting sensitive and individual-level data.
• Assist in developing scoring, segmentation, propensity, and predictive models using large-scale datasets.
• Explore identity-linked datasets and graph-based analytical approaches to understand relationships, patterns, and audience behavior.
• Contribute to analytical workflows involving secure data collaboration environments and data clean rooms, where applicable.
• Follow data privacy, security, governance, and responsible data handling practices throughout the model development lifecycle.

QUALIFICATIONS

Required Qualifications

• Bachelor's degree in Statistics, Computer Science, Data Science, Applied Mathematics, Economics, Engineering, or a related field.
• 3+ years of experience in Data Science, Applied Statistics, Machine Learning, or Analytics.
• Proficiency in Python or R and strong working knowledge of SQL for data extraction, transformation, and analysis.
• Understanding of statistical modeling, probability, hypothesis testing, regression analysis, and experimental design.
• Familiarity with causal inference concepts and the ability to interpret analytical results in a business context.
• Experience with exploratory data analysis, feature engineering, model development, and model validation.
• Ability to work with large, complex, multi-source datasets using cloud and data platforms such as Snowflake, Databricks, or AWS.
• Understanding of machine learning fundamentals, model evaluation techniques, and analytical best practices.
• Strong problem-solving, critical-thinking, and data interpretation skills.
• Excellent communication skills, with the ability to explain analytical findings and recommendations clearly.

Preferred Qualifications

• Exposure to marketing measurement methodologies, including incrementality testing, lift analysis, multi-touch attribution (MTA), marketing mix modeling (MMM), or rules-based attribution.
• Familiarity with marketing and media metrics such as impressions, reach, frequency, conversion rates, and attribution windows.
• Internship or professional experience in AdTech, MarTech, digital marketing analytics, customer analytics, or data-driven product environments.
• Exposure to identity resolution, graph-based data structures, identity-linked analytics, or privacy-preserving data clean rooms.
• Familiarity with data visualization and business intelligence tools such as Tableau, Looker, or Power BI.
• Experience working with external data sources, APIs, partner integrations, publishers, or retail media platforms.
• Coursework, certifications, or project experience in causal inference, A/B testing, experimentation, or statistical research.
• Familiarity with machine learning libraries such as scikit-learn, pandas, NumPy, and relevant statistical modeling packages.
• Exposure to LLMs, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP), and AI-assisted development tools such as GitHub Copilot or Claude Code.
• Understanding of best practices for validating AI-generated code, ensuring analytical accuracy, and using AI-assisted workflows responsibly.