Role Description As a Data Scientist II, you will leverage theory, data, and research to solve business problems. You will support data science and analytics efforts across multiple areas of the business including Sales, Marketing, Finance, HR, and related functions. You will contribute to building and improving measurement and reporting processes. To this end, you will help teams access insights needed to operate effectively. This role will be responsible for: - Drawing insights from large data sets. - Defining and implementing key model performance indicators. - Communicating insights and trends to support business decision-making. - Working closely with business stakeholders on measurement, success metrics, and analytics. - Using data to develop insights, forecasts, metrics, dashboards, and recommendations. Effectiveness in this position will require an understanding of technical methods and data engineering necessary to build and implement data science models, as well as knowing general industry trends, business objectives, and workforce dynamics. What You’ll Do (Essential Functions): - Contribute to data science projects supporting Sales, Marketing, Finance, HR, and related functions, collaborating with other team members. - Apply an understanding of business operations to translate defined requirements into data science tasks and KPIs. - Identify opportunities where data science can support team objectives. - Translate and summarize data into written reports, tables, graphs, dashboards, and charts. - Perform data preprocessing, feature engineering, and model selection for routine problems. - Design and implement AI models and pipelines based on documented requirements. - Analyze model performance using standard evaluation metrics. - Use distributed processing systems (e.g., Snowflake, Databricks, Google Cloud Platform). - Proactively identify and resolve issues in pipeline development and deployment. - Write understandable, modular code by applying established software development practices and style guides. - Use common data science libraries to implement designed solutions efficiently. - Participate in code reviews at critical points to validate that code meets requirements and standards. - Create initial technical documentation. The information in this job description represents a summary of the role and is not intended to be a comprehensive list of job duties. Responsibilities and duties of the position may change without notice at the Company’s discretion. Qualifications - Master's degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related). - 2+ years of professional experience building and deploying data science or machine learning solutions, including at least one production deployment. - Demonstrated experience translating business questions into data science problems and communicating findings to technical and non-technical audiences. - Proficiency in Python and SQL, and experience with data visualization tools such as Tableau. - Experience working with cloud data and ML platforms such as Snowflake, Databricks, or Google Cloud Platform. - Working knowledge of applied statistical methods and machine learning techniques (e.g., regression, classification, time series, cross-validation, model evaluation). Preferred Qualifications - Doctoral degree in a STEM or quantitative field (statistics, computer science, mathematics, economics, engineering, or related). - Familiarity with MLOps practices — model versioning, monitoring, drift detection, CI/CD for ML. - Experience designing and maintaining dashboards for operational or executive audiences. - Experience presenting analyses to senior technical and non-technical audiences. - Exposure to Sales, Marketing, Finance, or HR analytics domains. Company Description ZoomInfo (NASDAQ: GTM) is the Go-To-Market Intelligence Platform that empowers businesses to grow faster with AI-ready insights, trusted data, and advanced automation. Its solutions provide more than 35,000 companies worldwide with a complete view of their customers, making every seller their best seller. ZoomInfo is committed to protecting your privacy when you apply for jobs with us. Please review our Job Applicant Privacy Notice for more details on how we handle your personal information. ZoomInfo may use a software-based assessment as part of the recruitment process. More information about this tool, including the results of the most recent bias audit, is available here. ZoomInfo is proud to be an equal opportunity employer, hiring based on qualifications, merit, and business needs, and does not discriminate based on protected status.

• Develop and maintain Python-based data pipelines that collect, process, and ingest data from multiple sources. • Improve existing data workflows with a focus on quality assurance, sustainability, documentation, and maintainability. • Ensure analytic workflows are reproducible, well-structured, and scalable. • Support responsible handling of sensitive data in compliance with FERPA and AACOM data governance standards. • Develop dashboards and data visualization tools primarily using Tableau and Python. • Maintain and enhance existing dashboards, internal analytics tools, and client-facing data products. • Translate complex datasets into accessible and actionable visual insights. • Translate defined research designs into executable analytic workflows. • Clean, transform, and validate complex administrative and survey datasets. • Conduct quantitative analyses with increasing independence over time. • Contribute to applied research initiatives including workforce studies, survey analysis, and text-based or NLP projects. • Support the development and operationalization of statistical and/or ML models used in research and operational initiatives. • Partner with colleagues across AACOM departments to support strategic data and analytics initiatives. • Contribute to a culture of innovation, continuous learning, and process improvement. • Document workflows, methodologies, and technical processes clearly and consistently. • Stay current on emerging tools, technologies, and best practices in data science and analytics.

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources. • Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity. • Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards. • Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships. • Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

• Build and maintain new data models that are accurate and reliable • Work closely with a wide range of stakeholders from Data Science, Commercial Leadership, Sales Operations, Product, Design, and Engineering • Define, prototype, implement and improve ML models • Design experiments to test hypotheses (e.g. which customers should we focus on in our outbound calls) • Build/maintain reports, dashboards, and metrics to support our north star metrics • Teach and learn from their teammates. We value making others successful