Senior Data Scientist — Applied Analytics; Data & AI

About The Job

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

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.

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.

• 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.

• 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 Git Hub 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.

• 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.

• 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.

• 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…