*Red Hat will not be providing visa sponsorship for this position. Therefore, in order to be considered for this position, you must have the ability to work without a need for current or future visa sponsorship.

About the Job

The Senior Data Scientist on Applied Analytics drives data-driven decision-making and shapes approaches across high-priority data projects. Sitting at the intersection of our enterprise data platform and first-party datasets, this role resolves complex data issues and manages the data pipelines that power renewals, lifecycle, and sales activation. Seniors exercise good judgment on data modeling and quality, working with minimal instruction to transition from reactive reporting to proactive insights that integrate directly into the business workflow.

Note: This role may come into contact with confidential or sensitive customer or sales information requiring treatment in accordance with Red Hat policies and applicable privacy laws.

What You Will Do

• Lead Strategic Programs:Drive end-to-end data initiatives from problem framing and experimental design to delivery, including proof-of-concepts, stakeholder validation, and handoff to production-style patterns (orchestrated pipelines, dbt models, and production-grade data products).
• Architect Decision Logic:Refine the datasets and logic supporting strategic motions, such as funnel engagement behavior, cross-sell/risk signals, and adoption analytics for high-visibility sales programs.
• Deep Cross-Functional Partnership:Collaborate across Data & AI and the business (Product, GTM, Marketing and Sales) to resolve ambiguity and align on trade-offs regarding scope, quality, and compliance.
• Advance Responsible AI & Methodology:Apply LLM-assisted methods to accelerate synthesis and code development while owning the validation, reproducibility, and human-in-the-loop review for all outputs affecting business, customer and partner stakeholders.
• Communicate with Impact:Translate advanced technical work and novel methodologies into clear, jargon-free recommendations for senior leadership to facilitate data-driven decision-making.
• Elevate Technical Standards:Mentor analysts and data scientists on analysis design, statistical rigor, and stakeholder management; guide the team through enterprise platform norms such as masking and data-product operationalization.

What You Will Bring

Technical Skills & Tooling

• Programming Proficiency: Strong mastery of Python (specifically Pandas and enterprise cloud libraries) and expert-level SQL (Snowflake/DBeaver environments).
• AI Fluency: Comfort treating AI as a primary development collaborator, using prompt engineering and modern IDEs to increase coding velocity and automate manual tasks.
• Data Ops & Automation: Solid experience with GitHub workflows and a process-engineering mindset-you enjoy building automated data validation scripts to proactively catch and prevent recurring data issues.
• Statistics & Modeling: Solid practical knowledge of regression, simulation, scenario analysis, clustering, and decision trees applied to real-world business problems.
• Visualization: Ability to build clear, scannable data narratives across various mediums (slide decks, dashboards, and reporting frameworks) using at least one major enterprise BI platform.

Experience & Domain Expertise

• Professional Experience: 5-8+ years of professional experience manipulating large datasets, building analytical pipelines, and deploying statistical or predictive models.
• Business Acumen: Experience operating within tech/SaaS business models-ideally supporting Sales Operations, Finance, GTM strategy, or lifecycle analytics-is highly preferred.
• Education: Bachelor's degree in Statistics, Mathematics, Computer Science, or a related quantitative field.

The Mindset

• You are comfortable dealing with ambiguity and can navigate fast-paced environments where the business logic hasn't been fully defined yet, using pattern recognition to structure and execute solutions.

Success Looks Like:

• Driving Behavioral Change: Delivering highly credible, repeatable data applications and prescriptive insights that directly influence business decisions.
• Data Integrity: Building and maintaining clean, documented, and rigorous metric definitions within your project domains.
• Consistent Delivery: Ensuring predictable project execution through early identification of technical blockers and scope constraints.
• Collaborative Growth: Strengthening the team's overall output through active participation in code reviews, technical documentation, and shared engineering standards.

#LI-HM1

The salary range for this position is $118,600.00 - $195,680.00. Actual offer will be based on your qualifications.

Pay Transparency

Red Hat determines compensation based on several factors including but not limited to job location, experience, applicable skills and training, external market value, and internal pay equity. Annual salary is one component of Red Hat's compensation package. This position may also be eligible for bonus, commission, and/or equity. For positions with Remote-US locations, the actual salary range for the position may differ based on location but will be commensurate with job duties and relevant work experience.

Benefits
• Comprehensive medical, dental, and vision coverage
• Flexible Spending Account - healthcare and dependent care
• Health Savings Account - high deductible medical plan
• Retirement 401(k) with employer match
• Paid time off and holidays
• Paid parental leave plans for all new parents
• Leave benefits including disability, paid family medical leave, and paid military leave
• Additional benefits including employee stock purchase plan, family planning reimbursement, tuition reimbursement, transportation expense account, employee assistance program, and more!

Note: These benefits are only applicable to full time, permanent associates at Red Hat located in the United States.