Why This Job is Featured on The SaaS Jobs
This Data Scientist role sits at the intersection of product analytics and go to market systems, a common frontier in modern SaaS where growth is increasingly driven by automation, targeting, and decision engines rather than one off campaigns. The emphasis on internal, product like tooling and agentic workflows reflects how SaaS organizations operationalize customer engagement at scale, especially when serving large volumes of SMB accounts.
For a SaaS career, the durable value here is learning to quantify incremental impact in always on environments where clean A B tests are not always available. Building measurement frameworks, instrumentation, and monitoring around launches develops a repeatable skill set that transfers across lifecycle, sales assisted motions, and product led growth teams. The work also strengthens the ability to turn analysis into shipped changes, a differentiator for data scientists in SaaS who need to influence roadmaps and operating cadence.
This role tends to fit professionals who enjoy ambiguous problem spaces and prefer partnering closely with product managers and engineers rather than working as an isolated analytics function. It will suit someone comfortable balancing statistical rigor with pragmatic decision making, and who is motivated by building feedback loops that keep systems accountable over time.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Role
We’re hiring a Data Scientist to support our Marketing Innovation pod, a cross-functional team building the internal tools and agentic systems that fundamentally change how we do marketing and serve customers.
We build product-like systems that:
Deliver high-touch, consultative experiences to millions of SMB customers through agentic lifecycle and sales experiences
Adapt messaging, creative, and outreach using real-time behavioral signals
Power intelligent routing, targeting, and engagement decisions at scale, with minimal human-in-the-loop
In this role, you’ll be embedded with Product and Engineering to ensure these systems drive measurable business outcomes.
What You’ll Do
Define success metrics for agentic marketing systems (e.g., incremental pipeline generated, conversion lift, rep hours saved), including leading indicators that enable weekly iteration.
Design measurement and experimentation frameworks for always-on systems across lifecycle automation, creative generation, targeting, and routing — using holdouts, staged rollouts, and quasi-experimental methods when needed.
Partner with PMs and engineers to instrument, evaluate, and monitor launches so every meaningful release has observability and a credible read on incremental value.
Translate behavioral and model-driven signals into decisions: what to scale, where to intervene, and how to allocate human and compute attention across segments.
Build repeatable decision loops (pre-launch criteria → post-launch read → next action) that convert analysis into shipped changes.
What We’re Looking For
10+ years in a quantitative role (e.g., Data Science, Decision Science), ideally at a product-led company supporting B2B growth, with exposure to SMB or scaled self-serve motions.
Deep grounding in experimentation, causal inference, and applied statistics, with experience designing and interpreting tests in real-world, always-on environments.
Strong technical fluency in SQL and Python, including working directly with messy, incomplete behavioral data to quantify impact.
Proven track record of translating results into shipped decisions (product, lifecycle, targeting, routing).
Strong business judgment and a bias toward action: able to scope ambiguous problems, define success, and move quickly from insight to strategy.
Excellent communicator and partner to PMs/Engineers; comfortable influencing stakeholders and presenting recommendations to senior leadership.
Nice to Have
Familiarity with large language models and AI-assisted operations platforms
Experience working on operational automation and decision systems (routing, prioritization, optimization)
Experience operating in early-stage or rapidly evolving environments, including building measurement and experimentation frameworks from scratch.
About OpenAI
OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.
We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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