Materials Data Scientist / Scientific Software Developer

Enthought, Inc.; Austin (TX); Hybrid; USD 110,000 - 150,000; Full time

Benefits offered by this job

Meaningful impact

AI tooling exposure

World-class colleagues

Continuous learning

Global offices

Hybrid work

Competitive compensation

Job summary

Enthought, Inc. is hiring a Materials Data Scientist / Scientific Software Developer to join our client-facing solution delivery team.

You will combine deep materials science and chemistry with AI coding tools to build data-driven models, AI-based decision-support tools, and analysis pipelines for industrial R&D. Collaborate with client researchers and business leaders to design end-to-end informatics solutions, deploy GUI, 2D/3D graphics, and cloud-enabled workflows, and contribute to secure

Job description

We are hiring a Materials Data Scientist / Scientific Software Developer to join our client-facing solution delivery team. Working agent-first, you will pair deep materials and chemistry expertise with agentic AI coding tools to help industrial materials and chemical researchers make better, faster R&D decisions — building data-driven models, AI-based decision-support tools, and analysis pipelines.

Key Responsibilities

• Design end-to-end materials informatics solutions — apply deep knowledge of materials science, chemistry, and machine learning to translate client R&D problems into architectures spanning data, models, and user-facing applications.
• Work agent-first — use AI coding assistants and autonomous coding agents as your default way to explore data, prototype models, build software, write tests, and produce documentation, compressing the path from research question to working solution.
• Direct AI agents like a technical lead — decompose materials and chemistry problems into well-scoped tasks, give agents the domain context and physical constraints they need, and iterate toward correct, defensible results.
• Solve complex research and product-development problems in close collaboration with client researchers and business leaders.
• Build data-driven models, AI-based decision-support tools, and automated analysis pipelines, and assemble them into web-based solutions involving GUIs, 2D and 3D graphics, and cloud computing.
• Design AI-enabled and agentic capabilities into client solutions where they add value — including retrieval over materials data — held to the same rigor as any other component.
• Apply judgment about where AI and generative approaches fit and where they don’t, especially given the small, sparse, and noisy datasets typical of materials R&D.
• Take part in collaborative development practices — code review, architecture reviews, sprint planning, retrospectives, and daily standups — and interface directly with clients to define, demonstrate, and refine solutions.
• Handle client data and intellectual property responsibly, using AI tools only in approved, secure configurations.

What We Offer

• Meaningful impact — your work directly advances scientific breakthroughs, from accelerating drug discovery to developing sustainable materials.
• A front-row seat to agentic AI — work at an automation-first company where every role directs AI tools, and help define how the industry puts them to work.
• World-class colleagues — collaborate with some of the smartest, kindest scientists and engineers around.
• Continuous learning — access to Enthought’s training programs in Python, machine learning, and scientific computing.
• A global, collaborative culture across our Austin, Cambridge, and Tokyo offices.
• Flexible hybrid work based out of our Austin office.
• Competitive compensation and benefits.

Who Thrives Here

Everyone at Enthought shares a common set of traits: exceptional integrity, analytic intelligence, a sense of urgency, the ability to simplify the complex, openness in communication, empathy for people, and endurance. If you are endlessly curious, take full ownership of your work, and are excited to help shape how science gets done in the era of agentic AI, you will feel at home here.