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About This Role
- 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
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- 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
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- 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
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- 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:
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- 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.
About Red Hat
Red Hat is the world’s leading provider of enterprise open source software solutions, using a community\-powered approach to deliver high\-performing Linux, cloud, container, and Kubernetes technologies. Spread across 40\+ countries, our associates work flexibly across work environments, from in\-office, to office\-flex, to fully remote, depending on the requirements of their role. Red Hatters are encouraged to bring their best ideas, no matter their title or tenure. We're a leader in open source because of our open and inclusive environment. We hire creative, passionate people ready to contribute their ideas, help solve complex problems, and make an impact.
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.
Inclusion at Red Hat
Red Hat’s culture is built on the open source principles of transparency, collaboration, and inclusion, where the best ideas can come from anywhere and anyone. When this is realized, it empowers people from different backgrounds, perspectives, and experiences to come together to share ideas, challenge the status quo, and drive innovation. Our aspiration is that everyone experiences this culture with equal opportunity and access, and that all voices are not only heard but also celebrated. We hope you will join our celebration, and we welcome and encourage applicants from all the beautiful dimensions that compose our global village.
Equal Opportunity Policy (EEO)
Red Hat is proud to be an equal opportunity workplace and an affirmative action employer. We review applications for employment without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, citizenship, age, veteran status, genetic information, physical or mental disability, medical condition, marital status, or any other basis prohibited by law.
### Red Hat does not seek or accept unsolicited resumes or CVs from recruitment agencies. We are not responsible for, and will not pay, any fees, commissions, or any other payment related to unsolicited resumes or CVs except as required in a written contract between Red Hat and the recruitment agency or party requesting payment of a fee.
### Red Hat supports individuals with disabilities and provides reasonable accommodations to job applicants. If you need assistance completing our online job application, email application\[email protected]. General inquiries, such as those regarding the status of a job application, will not receive a reply.
Salary Context
This $118K-$195K range is below the median for Data Scientist roles in our dataset (median: $162K across 211 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,824 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Red Hat, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $200,000 based on 697 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 21% below the category median. Disclosed range: $118K to $195K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Red Hat AI Hiring
Red Hat has 2 open AI roles right now. They're hiring across Research Engineer, Data Scientist. Positions span Boston, MA, US, Raleigh, NC, US. Compensation range: $195K - $195K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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