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Climate Modelling Jobs in Maryland (NOW HIRING)

Inventwood

R & D Data Analyst

Frederick, MD · On-site

It's an analytical role embedded in the R&D team at an ARPA-E-backed climate-materials company: you ... modelling mathematical equations, and clearly documents assumptions, methods, and limitations so ...

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Climate Modelling information

What is climate modelling?

Climate modelling is the use of computer-based mathematical models to simulate the Earth's climate system, including the atmosphere, oceans, land surface, and ice. These models help scientists understand past, present, and future climate conditions by analyzing interactions between various components of the climate. Climate models are essential tools for predicting future climate changes, assessing the impact of human activities, and informing policy decisions related to climate change mitigation and adaptation.

What are some typical collaborative projects that climate modellers work on with other scientists?

Climate modellers frequently collaborate with meteorologists, oceanographers, data scientists, and policy experts to create comprehensive simulations and forecasts. These joint projects might include developing regional climate impact assessments, evaluating mitigation strategies, or contributing to international reports like those from the IPCC. Working in interdisciplinary teams allows climate modellers to integrate diverse data sets and perspectives, enhancing the accuracy and relevance of their models. Collaboration is essential for translating complex climate data into actionable insights for stakeholders and policymakers.

What are the key skills and qualifications needed to thrive as a climate modeller, and why are they important?

To thrive as a Climate Modeller, you need a strong background in atmospheric sciences, mathematics, and programming, typically supported by an advanced degree in climate science, meteorology, or a related field. Proficiency with climate modelling software (such as WRF or CESM), data analysis tools (like Python, MATLAB, or R), and experience with high-performance computing environments are essential. Strong analytical thinking, problem-solving abilities, and effective communication skills help translate complex model outputs into actionable insights. These skills and qualities are crucial for producing accurate climate projections that inform policy decisions and scientific understanding.

What is the difference between Climate Modelling vs Climate Data Analysis?

AspectClimate ModellingClimate Data Analysis
Required CredentialsBachelor's or Master's in Environmental Science, Meteorology, or related fields; programming skillsBachelor's or Master's in Data Science, Statistics, or related fields; strong analytical skills
Work EnvironmentResearch labs, universities, government agencies; computational and simulation workData centers, research institutions, consulting firms; data processing and interpretation
Employer & Industry UsageClimate research, environmental agencies, academiaEnvironmental consulting, research, policy analysis

Climate Modelling involves creating simulations to predict climate patterns using complex models, while Climate Data Analysis focuses on interpreting existing climate data to identify trends and insights. Both roles require strong analytical skills but differ in their approach—modeling emphasizes simulation, whereas data analysis emphasizes data interpretation.

What are popular job titles related to Climate Modelling jobs in Maryland?

For Climate Modelling jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Climate Modelling jobs in Maryland look for?

The top searched job categories for Climate Modelling jobs in Maryland are:

Climate Modelling jobs near you
Infographic showing various Climate Modelling job openings in Maryland as of August 2026, with employment types broken down into 81% Full Time, 15% Part Time, 2% Contract, and 2% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution.

R & D Data Analyst

Frederick, MD • On-site

Inventwood
Nanotechnology Research and Development • 1 - 10 employees
Learn more about Inventwoodexternal

Full-time

Re-posted 24 days ago

Key responsibilities

  • Analyzes experimental datasets, builds and maintains regression models, and documents assumptions, methods, and limitations.

  • Synthesizes analysis findings into summaries, visualizations, and recommendations for the research team.

  • Works in the lab to observe experiments, assist with data generation, and understand materials and processes firsthand.

Job description

General Summary / Position Purpose

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.

Job Duties and Responsibilities
  • 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.
Minimum Knowledge, Skills, and Abilities Required
  • 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.