In this role, you'll have end-to-end ownership of the architecture and technical strategy behind automation and agentic workflows that reduce mean time to mitigate (MTTM), boost quality, and enable our Support organization to scale impact without scaling headcount. You'll work hands-on with teams across Support, Product, and Platform Engineering to build seamless systems that anticipate customer needs before they arise.

You'll lead the technical foundation that transforms how customers experience support , where issues are auto-diagnosed, solutions are delivered instantly, and engineers focus their time on the toughest challenges. Your success will mean customers moving faster, trusting Databricks deeper, and feeling the impact of your systems every day.

Key responsibilities include:

• Owning the technical vision and architecture for Databricks' Support Automation and Tooling ecosystem
• Leading hands-on development of automation to improve customer experience and Support scalability
• Driving rapid, iterative development while upholding quality, safety, and reliability standards
• Designing agentic workflows that evolve from human-in-the-loop to fully automated systems
• Implementing observability, transparency, and rollback mechanisms for AI-driven decisions
• Acting as the primary technical interface between Support, Product, and Platform Engineering to align technical roadmaps and unblock dependencies
• Setting a high engineering bar for quality, reliability, and maintainability in line with Databricks standards
• Mentoring engineers and SMEs across Software and Support Engineering functions

We're looking for someone with:

• A BS or higher degree in Computer Science or a related field
• Technical leadership experience in large projects similar to those described, including automation tooling, distributed systems, and APIs
• Extensive full-stack development experience
• Proven success designing and deploying production-grade automation in complex technical environments
• Hands-on experience with ML-assisted systems, decision support, or agentic automation
• Deep familiarity with distributed data platforms, developer tooling, and large-scale infrastructure systems
• Understanding of multi-cloud environments (AWS, Azure, GCP), compliance, and security constraints

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range for this role is $190,000-$261,250 USD.

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Location

San Francisco

Employment Type

Full time

Department

Data Science

Compensation

• $293K – $515K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

Benefits

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts

• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)

• 401(k) retirement plan with employer match

• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)

• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees

• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)

• Mental health and wellness support

• Employer-paid basic life and disability coverage

• Annual learning and development stipend to fuel your professional growth

• Daily meals in our offices, and meal delivery credits as eligible

• Relocation support for eligible employees

• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

OpenAI’s mission is to ensure AI benefits all of humanity. ChatGPT for Work supports that mission by helping more people access real leverage from AI in their day-to-day jobs—so they can spend less time on busywork and coordination, and more time on the work that’s meaningful and additive. We’re building an AI-native workspace where AI acts as a superassistant for everyday tasks and a coworker you can hand work off to—then review, edit, and approve with confidence.

About the Role

As the Data Scientist for ChatGPT for Work, you’ll shape product strategy through data: uncover the user problems most worth solving, form sharp hypotheses about what will move team and business outcomes, and influence what we build next by presenting compelling recommendations grounded in rigorous evidence. You’ll be the DRI for the Work insight → strategy → experiment → decision loop—defining what “success” means for teams, pinpointing the highest-leverage adoption and retention bottlenecks, and turning signals into clear product direction.

You’ll partner closely with Product, Engineering, Research, and Finance to ensure our metrics are trusted, our experimentation is rigorous, and our insights turn into shipped improvements.

Responsibilities

• Own the core KPI framework for ChatGPT for Work, spanning onboarding, activation, engagement, retention, and expansion, as well as quality/trust guardrails.

• Build end-to-end funnels that identify where individuals and teams succeed or get stuck, from first workspace setup through repeat usage and long-term team adoption and value creation.

• Define and operationalize “time-to-value” and collaboration loop metrics, and connect them to business outcomes.

• Design and evaluate experiments and rollouts to quantify the impact of product changes across key Work surfaces and flows.

• Partner with product and engineering teams to improve instrumentation, data quality, and metric definitions so decisions are fast and correct.

• Translate complex analysis into clear, compelling insights that shape product strategy and roadmap decisions.

• Help establish data science standards and best practices for measuring human–AI collaboration and AI-native work outcomes.

• Partner with other data scientists across the company to share learnings and raise the bar on measurement, experimentation, and decision-making.

Requirements

• 10+ years in data science / analytics in in high-velocity product environments

• Direct experience working on B2B products (SaaS, collaboration/workspace, developer tools, or enterprise)

• Expert SQL + strong Python

• Strong experimentation + causal inference judgment (incl. when clean A/B tests aren’t feasible)

• Strong product sense/taste: can turn messy signals into crisp hypotheses and roadmap direction

• Proven ability to inspire and influence PM/Eng/Design + leadership through data storytelling

• Autonomous operator who sets the insights/measurement agenda

• Excellent executive communication; thrives in ambiguous, fast-moving environments

• AI-native operator (non-negotiable): “super AI-pilled”—first to adopt new AI tools, uses them daily to increase throughput, and turns them into durable org workflows

Nice-to-haves

• Experience with agentic and/or AI-native B2B products (agents, copilots, workflow automation, AI collaboration)

• Experience measuring AI product quality, trust, and human-AI interaction signals

• Familiarity with enterprise admin/security constraints and how they shape adoption

• Experience with

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