Senior Data Scientist — Data Cloud Acceleration
About this role
Building models, analyses, and supporting machine learning components forms the core of daily work for the Data Cloud Acceleration team at Zeta Global. This individual contributor role requires independent ownership of defined deliverables across revenue and intelligence initiatives, spanning data preparation, modeling, validation, documentation, and delivery. Working alongside lead data scientists, application engineers, analysts, and business stakeholders, the person in this position translates business questions into analytical approaches while balancing statistical rigor with practical project timelines. Successful team members prioritize reliable business impact over technical novelty and know how to move quickly without sacrificing data trust or operational discipline.
What you will do
- Take a defined business problem and independently deliver a reliable model, analysis component, scoring workflow, or supporting dataset.
- Profile, cleanse, join, and validate noisy datasets while checking completeness, freshness, distributions, nulls, duplicates, and match rates.
- Develop practical solutions using statistical methods, machine learning, deep learning, or existing models and services where appropriate.
- Move useful work beyond the notebook by building reusable Python components, batch-scoring processes, APIs, or lightweight services.
- Establish baselines, select appropriate metrics, perform statistical reasonableness checks, reconcile unexpected results, and clearly document limitations.
- Collaborate with application and data teams to define data ingredients, test hypotheses, and integrate model outputs into usable experiences.
- Apply productivity tools such as Claude and Codex to accelerate coding, testing, research, debugging, and documentation while verifying results independently.
What you need
- Apply statistical analysis and machine learning to solve real business problems.
- Demonstrate strong Python skills alongside familiarity with libraries such as pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow.
- Utilize strong SQL skills to analyze large datasets effectively.
- Bring experience with cloud data platforms like Snowflake, Databricks, Athena, Hive, BigQuery, or similar technologies.
- Develop classification, regression, clustering, forecasting, recommendation, optimization, or anomaly-detection solutions.
- Communicate analytical results and trade-offs clearly to both technical and nontechnical stakeholders.
Nice to have
- Familiarity with model evaluation, experimental design, feature engineering, and statistical validation
- Experience creating repeatable batch-scoring workflows or exposing model outputs through APIs or services
- Familiarity with orchestration and automation tools such as Airflow, AWS Glue, Prefect, or similar platforms
- Experience using version control, testing, and reproducible development practices
- Meaningful use of GenAI tools to improve the speed and quality of day-to-day work
Benefits
- Excellent medical, dental, and vision coverage
Skills and tech stack
Questions candidates ask
What team will I be working with in this role?
You will work in the Data Cloud Acceleration team across varied revenue and intelligence initiatives, often in partnership with a Lead Data Scientist, application engineers, analysts, and business stakeholders.
What level of independence is expected for deliverables?
You are expected to independently own defined deliverables from understanding the requirement and preparing the data through modeling, validation, documentation, and delivery without requiring step-by-step direction.
What tools and technologies are used in the tech stack?
The work involves Python libraries like pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow, strong SQL skills, and cloud data platforms such as Snowflake, Databricks, Athena, Hive, or BigQuery.
What benefits are included with the position?
The role includes excellent medical, dental, and vision coverage.