{"id":548018,"url":"https://alion.io/job/zeta-global-senior-data-scientist-data-cloud-acceleration","title":"Senior Data Scientist — Data Cloud Acceleration","company":{"id":5444,"name":"Zeta Global","domain":"zetaglobal.com","url":"https://alion.io/company/zeta-global","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":85,"open_postings":47,"ghost_share":0,"stale_share":0.404,"repost_share":0,"time_to_fill_p50_days":78,"computed_at":"2026-10-27T05:46:45Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Berlin, Germany","Prague, Czech Republic","Copenhagen, Denmark"],"countries":["DE","CZ","DK"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":32000,"max_usd":83000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2679},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS Glue","optional":false},{"name":"Claude","optional":false},{"name":"Databricks","optional":false},{"name":"Google BigQuery","optional":false},{"name":"LightGBM","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false}],"status":"live","first_seen_at":"2026-09-08T13:33:46Z","employer_posted_date":"2026-10-20","last_verified_at":"2026-10-27T14:09:01Z","board_verified":true,"closed_at":null,"days_open":49,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":49},"description":"WHO WE ARE \nZeta 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), our 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. Our 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. To learn more, go to www.zetaglobal.com.\nAbout the team \nThe Data Cloud Acceleration team identifies gaps and opportunities across clients and business units, then moves quickly to deliver practical new capabilities. We often develop and deploy the first version of a model, workflow, dataset, or application in days or weeks, learn from real usage, and improve it iteratively. \nWe are business-minded technologists who care more about impact than technical novelty. We use sophisticated methods when the problem requires them and simpler approaches when they will deliver a better result faster. Our work should be predictable, demoable, trusted, reusable, measured, and amplified by AI. \nAbout the role \nThe 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. \nThis 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. \nWhat you’ll do\nOwn model and analysis deliverables. Take a defined business problem and independently deliver a reliable model, analysis component, scoring workflow, or supporting dataset. \n\nTranslate 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. \n\nBuild and test models quickly. Develop practical solutions using statistical methods, machine learning, deep learning, or existing models and services where appropriate. \n\nPrepare trustworthy data. Profile, cleanse, join, and validate noisy datasets while checking completeness, freshness, distributions, nulls, duplicates, and match rates. \n\nCreate repeatable scoring workflows. Move useful work beyond the notebook by building reusable Python components, batch-scoring processes, APIs, or lightweight services. \n\nEvaluate results responsibly. Establish baselines, select appropriate metrics, perform statistical reasonableness checks, reconcile unexpected results, and clearly document limitations. \n\nSupport 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. \n\nAdd operational discipline. Include validation, monitoring, failure handling, refresh expectations, documentation, and a clear usage path in delivered work. \n\nUse 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. \n\nCommunicate progress early and clearly. Make milestones, assumptions, risks, dependencies, and issues visible rather than waiting until delivery. \n\nWhat ownership looks like at this level\nYou are expected to own the deliverable. That means: \nWorking independently on a well-defined model, workflow, dataset, or component \n\nProducing reliable and repeatable outputs rather than one-time analyses \n\nAdding appropriate validation and basic monitoring \n\nDocumenting assumptions, methodology, limitations, and usage \n\nDemonstrating the output and explaining how it supports the business \n\nRaising risks and ambiguity early \n\nLeaving the work in a condition that another team member can operate or extend \n\nYou 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. \nExperience that can set you up for success\nThese are useful indicators rather than absolute requirements: \nExperience applying statistical analysis and machine learning to real business problems \n\nStrong Python skills and familiarity with libraries such as pandas, scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow \n\nStrong SQL skills and experience analyzing large datasets \n\nExperience with cloud data platforms such as Snowflake, Databricks, Athena, Hive, BigQuery, or similar technologies \n\nExperience developing classification, regression, clustering, forecasting, recommendation, optimization, or anomaly-detection solutions \n\nFamiliarity with model evaluation, experimental design, feature engineering, and statistical validation \n\nExperience creating repeatable batch-scoring workflows or exposing model outputs through APIs or services \n\nFamiliarity with orchestration and automation tools such as Airflow, AWS Glue, Prefect, or similar platforms \n\nExperience using version control, testing, and reproducible development practices \n\nAbility to explain analytical results and trade-offs to technical and nontechnical stakeholders \n\nMeaningful use of GenAI tools to improve the speed and quality of day-to-day work \n\nYou’ll thrive here if you \nAre pragmatic. You select the simplest credible approach that can deliver useful business impact. \n\nMove quickly with discipline. You can produce an initial version rapidly while still validating the fundamentals. \n\nCare about trust. You check the data, question surprising results, and make limitations visible. \n\nWork well with ambiguity. You can turn an incomplete request into a clear set of questions, assumptions, and next steps. \n\nThink beyond the notebook. You consider how a model will be refreshed, accessed, demonstrated, monitored, and reused. \n\nUnderstand the business context. You evaluate technical decisions through the lens of client outcomes, revenue, cost, adoption, and decision quality. \n\nAre 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. \n\nEarly indicators of success \nWithin the first several months, a successful candidate will have: \nIndependently delivered a model, analysis workflow, or scoring component used in an active business or intelligence initiative \n\nAdded repeatable validation and documentation to their deliverables \n\nConverted at least one exploratory analysis or prototype into a reusable workflow \n\nDemonstrated clear understanding of how their work supports a client, product, revenue, or operational outcome \n\nUsed AI-assisted development to improve delivery speed without compromising accuracy or trust \n\nEarned confidence from data science, application, and business partners through reliable execution and clear communication \n\nBENEFITS & PERKS\nExcellent medical, dental, and vision coverage\nPEOPLE & CULTURE AT ZETA\nZeta 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. \nWe’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/ \nZETA IN THE NEWS!\nhttps://zetaglobal.com/press/?cat=press-releases","description_format":"text","description_chars":8453,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Marketing Automation","Email Marketing","Customer Data Platforms (CDP) & Reverse ETL"],"lifecycle":[{"event":"open","at":"2026-09-08T13:41:28Z"}],"visa":[],"liveness":{"score":61,"band":"ok","label":"Likely open","p_open":1,"p_active":0.848,"p_room":0.72,"age_days":48,"expected_fill_days":78,"reasons":["conf:0","velocity","win:mid","crowd:brand"],"computed_at":"2026-10-27T05:46:45Z"},"pay":null,"html_url":"https://alion.io/job/zeta-global-senior-data-scientist-data-cloud-acceleration","json_url":"https://alion.io/job/zeta-global-senior-data-scientist-data-cloud-acceleration.json","meta":{"generated_at":"2026-10-27T15:31:58Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":4421,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-28T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":5444},"rest":"https://alion.io/mcp/rest/get_company?id=5444"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fzeta-global-senior-data-scientist-data-cloud-acceleration"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fzeta-global-senior-data-scientist-data-cloud-acceleration"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fzeta-global-senior-data-scientist-data-cloud-acceleration"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/zeta-global-senior-data-scientist-data-cloud-acceleration\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fzeta-global-senior-data-scientist-data-cloud-acceleration"}]}