R & D Data Analyst

The R&D Data Analyst turns the data behind SUPERWOOD - a molecularly densified wood material - into the insights that drive our research forward. It’s an analytical role embedded in the R&D team at an ARPA-E-backed climate-materials company: you’ll analyze experimental data, build and apply models for processes like chemical infusion and thermal behavior, and work alongside the scientists generating the data - in the lab, not just behind a screen. This is InventWood’s first dedicated data role. The majority of your time will be spent on analysis - regression, process modeling, and helping the research team draw real value from the data they produce - with the balance spent in the lab, learning our materials and processes firsthand. Along the way you’ll help establish how we capture, structure, and use experimental data as the company grows. We’re looking for someone rigorous and curious who wants to help build a data practice from the ground up in a high-intensity, build-the-company environment., * Analyzes experimental datasets - builds and maintains regression models, comfortable with modelling mathematical equations, and clearly documents assumptions, methods, and limitations so results are reproducible and defensible.

• Turns analysis into decisions - synthesizes findings into clear summaries, visualizations, and recommendations the research team can act on, and presents them in team discussions.
• Works in the lab - spends regular time on the floor observing and assisting with experiments to understand firsthand how data is generated. This is not a heavy-machinery role, but comfort in a lab and manufacturing environment is essential.
• Improves how data is captured - helps standardize experiment logging, builds templates and lightweight workflows, and flags data-quality issues early, before they compromise a result.
• Contributes to study design - brings a data perspective to research planning conversations: identifying gaps in existing datasets, proposing follow-up experiments the data suggests, and helping the team narrow parameter spaces before physical testing.
• Builds the data practice - as InventWood’s first dedicated data hire, documents workflows and conventions so the practice scales cleanly as the team grows.
• Follows all safety policies, standards, and regulations; upholds and promotes company Core Values and culture; takes on special projects as directed by your manager; and maintains strict confidentiality.

• A BS in a quantitative or scientific field - statistics, data science, engineering, physics, chemistry, materials science, or related - and 3 - 5 years work experience in a related field.
• Strong working proficiency in Python for data analysis (e.g., pandas, SciPy, Jupyter, matplotlib/Plotly or similar).
• A solid grounding in regression analysis and applied statistics from a data analyst’s perspective - understanding model fit, uncertainty, and when a conclusion is (and isn’t) supported by the data. This is a requirement.
• Experience or demonstrated aptitude modeling physical processes is a strong plus.
• A materials science background is a plus, not a requirement - but genuine curiosity about the science is. You’ll be expected to learn our materials and processes well enough to ask good questions.
• Comfortable working in a lab environment and spending meaningful time alongside the research team, not just downstream of it.
• A builder’s mindset - comfortable with ambiguity, energized by establishing systems and conventions where none exist yet, and willing to do the unglamorous work (documentation, data hygiene, templates) that makes everything else possible.
• Communicates clearly in writing and in person; documents work so someone else can follow it.
• Team-oriented - brings energy, gets excited about building the company, and lifts the team. Detail-oriented, with strong emotional intelligence and humility.
• Fluent in English; able to work onsite 5 days a week in Frederick, MD.

Physical Demands

• Must be able to occasionally lift up to 25 pounds (samples, equipment).
• Must be able to be fitted for, and wear, appropriate personal protective equipment when in lab and production areas.