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About This Role
RDQ427R169
While candidates in the listed locations are encouraged for this role, we are open to remote candidates in other locations.
Databricks is looking for a Principal Data Scientist to serve as the statistical voice of the Data Science organization. This person will make Databricks smarter and more data\-driven at the highest levels of leadership — translating the full power of data science into clear, actionable narratives for our CEO, C\-suite, and Board of Directors.
Our vision is simple: data drives every Databricks decision and action. To get there, we need a world\-class statistician and communicator — someone who can bridge the gap between deep analytical rigor and executive decision\-making. This is a pure IC role with company\-wide influence: no direct reports, maximum leverage.
Databricks was founded in 2013 by the original creators of Apache Spark, Delta Lake, and MLflow. We built the Databricks Data Intelligence Platform to help organizations unify their data, analytics, and AI workloads. With more than 10,000 organizations worldwide — including over half of the Fortune 500 — relying on Databricks, and a team of 7,000\+ employees, we are at the forefront of the data and AI revolution. We have been recognized as a leader by Gartner, Forrester, and IDC, and have raised more than $4 billion in funding at a $62 billion valuation.
The Impact You Will Have
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- Executive translation. Translate complex data science findings into clear, actionable narratives for the CEO, C\-suite, and Board of Directors — ensuring data science insights directly inform the company's most critical decisions.
- Statistical authority. Serve as the company's chief statistical voice and the final quality backstop for analytical rigor in high\-stakes executive decisions. Advance the state\-of\-the\-art in how Databricks applies statistical methods to business problems.
- Org\-wide uplevel. Raise the communication bar across the entire Data Science organization by setting standards, coaching teams, and co\-authoring key executive\-facing deliverables. Make every DS team better at telling their story.
- Strategic insights. Produce deep strategic analyses on revenue, platform health, operational efficiency, and competitive positioning — the kind of synthesized, judgment\-rich insight that AI cannot autonomously create.
- Cross\-functional influence. Partner with engineering VPs, product leaders, and executive staff to embed a data\-driven decision\-making culture across the company. Be the trusted analytical advisor in rooms where critical decisions are made.
- External thought leadership. Represent Databricks externally as a data science thought leader at industry conferences, in publications, and in the broader statistical community. Build an external identity that attracts world\-class talent.
- Methodology and standards. Define and evolve company\-wide scientific methodologies — experimentation frameworks, forecasting systems, causal inference approaches — to match and push industry state\-of\-the\-art.
What We Look For
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### Required
- 15\+ years of experience in data science, statistics, or quantitative research spanning industry and/or academia.
- Proven track record of presenting statistical and data science concepts to C\-suite and Board\-level audiences, with measurable impact on executive decision\-making.
- Broad expertise across data science disciplines: experimentation, causal inference, forecasting, optimization, and machine learning.
- Exceptional written and verbal communication — the ability to make complex statistical concepts intuitive and compelling for non\-technical executives.
- Track record of upleveling teams: setting analytical standards, mentoring senior data scientists, and improving org\-wide output quality.
- Experience at the intersection of statistics and large\-scale technology or data platforms.
- Ph.D. in Statistics, Mathematics, Computer Science, or a related quantitative field.
### Preferred
- Academic research or teaching background in statistics or a quantitative field.
- Industry experience at multiple tier\-1 technology companies.
- Published research or recognized thought leadership in applied statistics.
- Experience building or leading a "Chief Statistician" or equivalent function.
- Experience in infrastructure, platform, or systems\-oriented data science.
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio\-economic status, veteran status, and other protected characteristics.
Compliance
If access to export\-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
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 2,799 AI roles we're tracking, Data Scientist positions make up 7% of the market. At Databricks, 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 $200,350 based on 604 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,500.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $252,000. 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: $97,760; Mid: $159,385; Senior: $227,500; Director: $242,000; VP: $250,000.
Databricks AI Hiring
Databricks has 28 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, AI Software Engineer, Research Scientist. Positions span San Francisco, CA, US, Seattle, WA, US, CA, US. Compensation range: $205K - $350K.
Location Context
Across all AI roles, 16% (460 positions) offer remote work, while 2,318 require on-site attendance. Top AI hiring metros: New York (2,241 roles, $208,300 median); San Francisco (1,822 roles, $252,000 median); Los Angeles (1,611 roles, $188,900 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 2,799 open positions tracked in our dataset. By seniority: 98 entry-level, 1,283 mid-level, 1,092 senior, and 326 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (460 positions). The remaining 2,318 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $252,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 30 roles); AI Safety ($274,200 median, 43 roles); Research Engineer ($260,000 median, 387 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 2,799 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (1,978), AI Software Engineer (197), Data Scientist (195). 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 (98) are outnumbered by mid-level (1,283) and senior (1,092) 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 326 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (460 positions), with 2,318 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 $252,000, 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 (1,433 postings), Aws (840 postings), Rag (663 postings), Azure (639 postings), Gcp (537 postings), Pytorch (445 postings), Prompt Engineering (418 postings), Claude (396 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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