Head of Data Science
RenderRemote: United StatesPosted 7 April 2026
Job Description
Head of Data Science
At Render, we’re building the modern cloud platform for developers creating AI-native, full-stack, multi-service applications. Our mission is to eliminate the tradeoff between the power of hyperscalers and the simplicity of developer-friendly platforms—so teams can ship fast, scale reliably, and focus on their product, not infrastructure.
Unlike complex hyperscalers or ephemeral edge/serverless solutions, Render offers a developer-first experience with persistent compute, dynamic autoscaling, built-in orchestration, and observability, allowing teams to launch, scale, and manage real-world applications without writing infrastructure code or managing servers. Whether you're building LLM-powered applications, scalable SaaS products, or async processing pipelines, Render empowers teams to move fast and scale confidently from MVP to millions of users.
Our platform is trusted by over 4.5 million developers worldwide and continues to grow rapidly. In February 2026, we raised an additional $100M in Series C financing, bringing our total funding to $257M, to accelerate our vision of making cloud infrastructure both powerful and intuitive—designed for the speed of modern AI development.
We’re a diverse and talented team that values craft, velocity, and user experience. If you’re excited to help shape the future of the intelligent cloud and empower developers everywhere, we’d love to hear from you.
APPLYING TO RENDER
We're seeking candidates who possess high integrity, humility, and an insatiable drive to learn. Through reasoned discussions and continuous feedback, we strive to improve both individually and collectively. We foster an environment of mutual trust and respect, empowering effective debate to achieve the best outcomes for our customers and team.
We especially encourage members of underrepresented groups in the tech community to apply and understand that not all successful candidates will meet each requirement listed.
Our interview process is unique to each role, and we value the candidate experience just as much as our customer experience. We hope your conversations with us reflect a thoughtful process that is illuminative, enjoyable, and respectful of your time.
ABOUT THE ROLE
We’re looking for a visionary and hands-on Head of Data Science to join our team. In this role, you’ll define and scale the data function at Render from the ground up—setting the strategy, building a world-class team, and embedding data deeply into every decision we make.
You will work closely with leaders across Product, Engineering, Marketing, and GTM to shape product strategy, surface key insights, and drive operational excellence. This is a unique opportunity to lead a highly impactful function at a pivotal stage of our growth.
You will lead by example, guiding the work of other data scientists while personally contributing to exploratory analysis, experiment design, causal inference, and data infrastructure improvements.
YOU WILL:
- Build, lead, and mentor a high-performing data science team across analytics, experimentation, and statistical modeling.
- Personally drive and guide impactful IC work, from ad hoc analysis to ML-based modeling projects.
- Create and evangelize best practices for data reliability, reproducibility, and documentation.
- Collaborate with senior leadership to define a long term data strategy
- Own and scale the Data Science function across product, growth, and go-to-market, aligning with company OKRs and strategic bets.
- Hire, lead, and mentor a world-class team of data scientists and analysts; build a high-trust, high-impact culture grounded in intellectual rigor and bias for action.
- Define and communicate Render’s long-term data strategy and architecture in partnership with Engineering and Product leadership.
- Drive strategic insight across the org: from pricing and packaging to product adoption, conversion, and customer segmentation.
- Promote data fluency across Render thr ... (truncated, view full listing at source)
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