Senior Data Scientist, Zeta Global
Data Cloud Acceleration
The AI marketing cloud
Posted today
The AI marketing cloud
1001+ employees
Posted today
1001+ employees
Company mission
To enable brands to build repeatable and scalable programs that accelerate business growth, efficiently, and effectively, and drive higher ROI on their marketing investments.
Role
Who you are
- 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
- 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
- Are pragmatic. You select the simplest credible approach that can deliver useful business impact
- Move quickly with discipline. You can produce an initial version rapidly while still validating the fundamentals
- Care about trust. You check the data, question surprising results, and make limitations visible
- Work well with ambiguity. You can turn an incomplete request into a clear set of questions, assumptions, and next steps
- 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
What the job involves
- 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
Our take
Zeta Global is a cloud-based AI marketing platform helping companies acquire, grow and retain customers more efficiently. Its flagship Zeta Marketing Platform (ZMP) combines identity resolution, consumer intelligence, and omnichannel activation into a single platform. Using one of the industry's largest proprietary database and advanced AI, Zeta makes personalized marketing simple for enterprise brands worldwide.
Its platform is particularly valuable in today's privacy-first, data-driven marketing environment. It helps brands understand individual customers and deliver relevant experience across every channel, from email and SMS to digital and social. Serving top advertisers and Fortune 500 companies, Zeta turns complex data into actionable insights, so markets have a deeper relationship with their customers and drive measurable results in a competitive landscape.
Recently, Zeta expanded its capabilities with acquisition of Marigold's enterprise software business, adding over 100 enterprise clients and new loyalty and engagement tools. This strengthens Zeta's "One Zeta" model, creates cross-selling opportunities, and extends its footprint in EMEA and APAC. Also backed by strong AI investments, a massive proprietary identity graph, and consistent revenue growth, Zeta is in a good position to help brands deliver smarter, more personalized marketing at scale for years to come.
Steph
Company Specialist at Welcome to the Jungle
Insights
11% employee growth in 12 months
Company
Funding (last 2 of 3 rounds)
Apr 2017
$115m
SERIES F
Jul 2015
$125m
LATE VC
Total funding: $310m
Company HQ
Midtown South, New York, NY
Leadership
David A Steinberg
(Co-Founder, CEO & Chairman of the Board)Prior to co-founding Zeta and CAIVIS, they founded InPhonic, the largest seller of wireless phones and communications products and services on the Internet. InPhonic was #1 on Inc. 500 list of fastest growing companies in 2004
John Sculley
(Co-Founder, Vice Chairman of ther Board & Investor)Former CEO of Apple Computer. They joined Apple in 1983 when the Company had $600 million in revenues; by the time they left, Apple's revenues exceeded $8 billion.
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