Senior Data Scientist — Data Cloud Acceleration at Zeta Global | AdTechTalent
Data Science6 days agoOn-site
Zeta Global
Senior Data Scientist — Data Cloud Acceleration
pythonmachine learningdata sciencestatistical analysissqlcloud data platformsmodel evaluationfeature engineeringbatch scoringapiorchestrationautomationgenaiai-assisted development
Zeta Global seeks a Senior Data Scientist to independently build models, analyses, and ML components that improve business decisions and client outcomes. Responsibilities include owning deliverables from requirement understanding through modeling, validation, documentation, and delivery; translating business questions into analytical approaches; building and testing models using statistical and machine learning methods; preparing and validating data; creating repeatable scoring workflows; evaluating results responsibly; supporting intelligence products; adding operational discipline; using AI tools to improve productivity; and communicating progress clearly. Required skills include strong Python and SQL abilities, experience with cloud data platforms (Snowflake, Databricks, Athena, Hive, BigQuery), model development (classification, regression, clustering, forecasting, recommendation, optimization, anomaly detection), model evaluation, feature engineering, batch scoring, API development, orchestration and automation tools (Airflow, AWS Glue, Prefect), version control, testing, and ability to explain results to technical and nontechnical stakeholders. Benefits include excellent medical, dental, and vision coverage. Locations include Berlin, Germany; Copenhagen, Denmark; and Prague, Czech Republic.
What you'll do
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Own model and analysis deliverables from understanding requirements through modeling, validation, documentation, and delivery
Translate business questions into analytical approaches
Build and test models quickly using statistical methods, machine learning, deep learning, or existing models and services
Prepare trustworthy data by profiling, cleansing, joining, and validating datasets
Create repeatable scoring workflows including reusable Python components, batch-scoring processes, APIs, or lightweight services
Evaluate results responsibly with baselines, metrics, statistical checks, and documentation of limitations
Support intelligence products and applications by working with application and data teams to define data ingredients, test hypotheses, and integrate model outputs
Add operational discipline including validation, monitoring, failure handling, refresh expectations, documentation, and usage paths
Use AI tools such as Claude and Codex to improve productivity while independently verifying results
Communicate progress early and clearly including milestones, assumptions, risks, dependencies, and issues
Requirements
Experience applying statistical analysis and machine learning to real business problems
Strong Python skills and familiarity with libraries such as pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
Strong SQL skills and experience analyzing large datasets
Experience with cloud data platforms such as Snowflake, Databricks, Athena, Hive, BigQuery, or similar technologies
