Z
Zeta Global
Senior Data Scientist (Data Cloud Acceleration)
Skills
SQL QueriesDeep LearningPythonData SciencepandasPyTorchTensorFlowSnowflakeDatabricksGitMachine LearningAIAWSApache Airflow
1 month ago
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Important information
Contract type:
Permanent
Location:
Berlin, Germany
Work mode:
Onsite
Published on:
11 September 2026
What they need
- The Senior Data Scientist will build models, analyses, and supporting ML components that improve business decisions, intelligence products, and client outcomes.
- You will independently own defined deliverables—from understanding the requirement and preparing the data through modeling, validation, documentation, and delivery
- This is a hands-on individual contributor role. You will work across varied revenue and intelligence initiatives, often in partnership with a Lead Data Scientist, application engineers, analysts, and business stakeholders.
- The right candidate can move quickly without sacrificing trustworthiness and knows how to balance statistical rigor with the practical needs of the business
- Own model and analysis deliverables. Take a defined business problem and independently deliver a reliable model, analysis component, scoring workflow, or supporting dataset
- Translate business questions into analytical approaches. Ask clarifying questions, understand how the output will be used, and recommend an approach that fits the decision, timeline, and available data
- Build and test models quickly. Develop practical solutions using statistical methods, machine learning, deep learning, or existing models and services where appropriate
- Prepare trustworthy data. Profile, cleanse, join, and validate noisy datasets while checking completeness, freshness, distributions, nulls, duplicates, and match rates
- Create repeatable scoring workflows. Move useful work beyond the notebook by building reusable Python components, batch-scoring processes, APIs, or lightweight services
- Evaluate results responsibly. Establish baselines, select appropriate metrics, perform statistical reasonableness checks, reconcile unexpected results, and clearly document limitations
- Support intelligence products and applications. Work with application and data teams to define the right data ingredients, test hypotheses, and integrate model outputs into usable experiences
- Add operational discipline. Include validation, monitoring, failure handling, refresh expectations, documentation, and a clear usage path in delivered work
- Use AI to improve your own productivity. Apply tools such as Claude, Codex, and similar assistants to accelerate coding, testing, research, debugging, and documentation while independently verifying the results
- Communicate progress early and clearly. Make milestones, assumptions, risks, dependencies, and issues visible rather than waiting until delivery
- You are expected to own the deliverable. That means:
- Working independently on a well-defined model, workflow, dataset, or component
- Producing reliable and repeatable outputs rather than one-time analyses
- Adding appropriate validation and basic monitoring
- Documenting assumptions, methodology, limitations, and usage
- Demonstrating the output and explaining how it supports the business
- Raising risks and ambiguity early
- Leaving the work in a condition that another team member can operate or extend
- You will receive guidance on broader product direction, methodology, and complex stakeholder decisions, but you should not require step-by-step direction to complete the work
- Within the first several months, a successful candidate will have;
- Independently delivered a model, analysis workflow, or scoring component used in an active business or intelligence initiative
- Added repeatable validation and documentation to their deliverables
- Converted at least one exploratory analysis or prototype into a reusable workflow
- Demonstrated clear understanding of how their work supports a client, product, revenue, or operational outcome
- Used AI-assisted development to improve delivery speed without compromising accuracy or trust
- Earned confidence from data science, application, and business partners through reliable execution and clear communication- Familiarity with orchestration and automation tools such as Airflow, AWS Glue, Prefect, or similar platforms
- Meaningful use of GenAI tools to improve the speed and quality of day-to-day work
- Strong Python skills and familiarity with libraries such as pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
- Experience with cloud data platforms such as Snowflake, Databricks, Athena, Hive, BigQuery, or similar technologies
- Experience using version control, testing, and reproducible development practices
- Experience developing classification, regression, clustering, forecasting, recommendation, optimization, or anomaly-detection solutions
- Ability to explain analytical results and trade-offs to technical and nontechnical stakeholders
- Experience creating repeatable batch-scoring workflows or exposing model outputs through APIs or services
- Strong SQL skills and experience analyzing large datasets
- Experience applying statistical analysis and machine learning to real business problems
- Familiarity with model evaluation, experimental design, feature engineering, and statistical validation
- Work well with ambiguity. You can turn an incomplete request into a clear set of questions, assumptions, and next steps
- Move quickly with discipline. You can produce an initial version rapidly while still validating the fundamentals
- Think beyond the notebook. You consider how a model will be refreshed, accessed, demonstrated, monitored, and reused
- Understand the business context. You evaluate technical decisions through the lens of client outcomes, revenue, cost, adoption, and decision quality
- Are curious and low-ego. You are comfortable learning from others, revising an approach, and using an existing solution when it is better than building a new one
- Are pragmatic. You select the simplest credible approach that can deliver useful business impact
- Care about trust. You check the data, question surprising results, and make limitations visible
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