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About the Role
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 where 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.
Responsibilities
- Own model and analysis deliverables, taking a defined business problem and independently delivering a reliable model, analysis component, scoring workflow, or supporting dataset.
- Translate business questions into analytical approaches, asking clarifying questions, understanding output usage, and recommending an approach that fits the decision, timeline, and available data.
- Build and test models quickly, developing practical solutions using statistical methods, machine learning, deep learning, or existing models and services.
- Prepare trustworthy data by profiling, cleansing, joining, and validating noisy datasets while checking completeness, freshness, distributions, nulls, duplicates, and match rates.
- Create repeatable scoring workflows by building reusable Python components, batch-scoring processes, APIs, or lightweight services.
- Evaluate results responsibly by establishing baselines, selecting appropriate metrics, performing statistical reasonableness checks, reconciling unexpected results, and clearly documenting limitations.
- Support intelligence products and applications by defining the right data ingredients, testing hypotheses, and integrating model outputs into usable experiences.
- Add operational discipline, including validation, monitoring, failure handling, refresh expectations, documentation, and a clear usage path in delivered work.
- Use AI to improve productivity by applying 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 by making milestones, assumptions, risks, dependencies, and issues visible.
Requirements
- Experience applying statistical analysis and machine learning to real business problems.
- Experience developing classification, regression, clustering, forecasting, recommendation, optimization, or anomaly-detection solutions.
- 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.
- Ability to explain analytical results and trade-offs to technical and nontechnical stakeholders.
- Meaningful use of GenAI tools to improve the speed and quality of day-to-day work.
- Pragmatic approach, selecting the simplest credible approach that can deliver useful business impact.
- Ability to move quickly with discipline, producing an initial version rapidly while still validating the fundamentals.
- Care about trust, checking data, questioning surprising results, and making limitations visible.
- Ability to work well with ambiguity, turning an incomplete request into a clear set of questions, assumptions, and next steps.
- Ability to think beyond the notebook, considering how a model will be refreshed, accessed, demonstrated, monitored, and reused.
- Understanding of business context, evaluating technical decisions through the lens of client outcomes, revenue, cost, adoption, and decision quality.
- Curious and low-ego, comfortable learning from others, revising an approach, and using an existing solution when it is better than building a new one.
Skills
- Python
- pandas
- scikit-learn
- XGBoost
- LightGBM
- PyTorch
- TensorFlow
- SQL
- Snowflake
- Databricks
- Athena
- Hive
- BigQuery
- Airflow
- AWS Glue
- Prefect
- GenAI tools
Location
- New York City
Work Type
- Full-time
Experience Level
- Senior
Benefits
- Excellent medical, dental, and vision coverage
About the Company
- Zeta Global (NYSE: ZETA) is the AI-Powered Marketing Cloud that leverages advanced artificial intelligence (AI) and trillions of consumer signals to make it easier for marketers to acquire, grow, and retain customers more efficiently.
- Through the Zeta Marketing Platform (ZMP), Zeta's vision is to make sophisticated marketing simple by unifying identity, intelligence, and omnichannel activation into a single platform – powered by one of the industry’s largest proprietary databases and AI.
- Enterprise customers across multiple verticals are empowered to personalize experiences with consumers at an individual level across every channel, delivering better results for marketing programs.
- Zeta was founded in 2007 by David A. Steinberg and John Sculley and is headquartered in New York City with offices around the world.
- The Data Cloud Acceleration team identifies gaps and opportunities across clients and business units, then moves quickly to deliver practical new capabilities.
- The team often develops and deploys the first version of a model, workflow, dataset, or application in days or weeks, learns from real usage, and improves it iteratively.
- The team consists of business-minded technologists who care more about impact than technical novelty, using sophisticated methods when the problem requires them and simpler approaches when they will deliver a better result faster.
- Their work should be predictable, demoable, trusted, reusable, measured, and amplified by AI.
Equal Opportunity
- Zeta considers applicants for employment without regard to, and does not discriminate on the basis of an individual’s sex, race, color, religion, age, disability, status as a veteran, or national or ethnic origin; nor does Zeta discriminate on the basis of sexual orientation, gender identity or expression.
- We’re committed to building a workplace culture of trust and belonging, so everyone feels invited to bring their whole selves to work.
- We provide a forum for employees to celebrate, support and advocate for one another.
- Learn more about our commitment to diversity, equity and inclusion here: https://zetaglobal.com/blog/a-look-into-zetas-ergs/