Senior Machine Learning Scientist, Climate & Hydrology

About the role

We are seeking a highly skilled Senior Machine Learning Scientist with a focus on Climate and Hydrology to lead the scientific and technical development of hydrology-centered climate modeling within our environmental modeling platform. This role requires a deep understanding of climate science, hydrology, and advanced machine learning techniques to create predictive systems that are scientifically robust and innovative. The successful candidate will work at the intersection of environmental science and artificial intelligence, applying their expertise to develop models that improve our understanding of climate and hydrological processes. This position offers an exciting opportunity to contribute to impactful environmental solutions by leveraging large datasets, cutting-edge ML methods, and interdisciplinary collaboration. The role involves guiding research initiatives, developing scalable models, and translating scientific insights into practical applications that support our company's mission to address environmental challenges.

Key facts

What you'll do

• Lead and oversee scientific and technical projects focused on hydrology, climate science, and machine learning to develop innovative predictive models.
• Define research priorities and establish modeling strategies that integrate physical sciences with machine learning techniques for environmental prediction.
• Enhance existing ML models by incorporating physical constraints, Earth system science insights, and domain-specific knowledge to improve accuracy and reliability.
• Utilize extensive climate, weather, hydrology, remote sensing, and observational datasets to inform model development, training, and validation processes.
• Build, optimize, and maintain reproducible workflows for data processing, model training, benchmarking, and performance evaluation to ensure scientific rigor.
• Collaborate closely with research scientists, data engineers, and software developers to translate scientific objectives into scalable, deployable solutions.
• Conduct comprehensive model assessments, including uncertainty quantification, sensitivity analysis, and validation against observational data to ensure scientific validity.
• Present research findings and technical developments through internal reports, scientific publications, conferences, and stakeholder meetings.
• Contribute to the strategic planning of the team's research agenda, fostering innovation and interdisciplinary collaboration.
• Mentor junior scientists and researchers, supporting their professional growth and fostering a collaborative research environment.
• Stay current with advancements in climate modeling, hydrology, machine learning, and related fields to continuously improve modeling approaches and methodologies.
• Support the integration of models into operational systems and assist in deploying solutions that can be used for real-world environmental decision-making.

Requirements

• PhD in machine learning, computational science, Earth science, atmospheric science, hydrology, AI, computer science, or a related quantitative discipline.
• Minimum of 5 years of postdoctoral, industry, or applied research experience in climate machine learning, weather modeling, hydrologic modeling, Earth system modeling, or a closely related field.
• Proven track record of developing and applying ML-enhanced models for weather, climate, or hydrology, with a significant publication record in peer-reviewed journals.
• Extensive experience working with large climate datasets, including reanalysis products, remote sensing data, observational datasets, and model outputs.
• Familiarity with the computational infrastructure necessary for managing, preprocessing, and training on large-scale climate datasets, preferably within the AWS ecosystem.
• Strong programming skills in Python, with experience in modern machine learning frameworks such as PyTorch or similar.
• Ability to develop and implement rigorous evaluation frameworks, performance metrics, and benchmarking strategies for environmental prediction systems.
• Knowledge of physical constraints, data assimilation, hybrid physics-ML methods, and spatiotemporal modeling techniques is preferred.
• Excellent written and verbal communication skills, capable of producing technical reports, scientific papers, and delivering presentations to diverse audiences.
• Demonstrated ability to work independently and collaboratively in a fast-paced, interdisciplinary research environment.
• Experience mentoring junior team members, leading research projects, or contributing to research strategy development is a plus.
• Familiarity with cloud computing environments, especially AWS, for large-scale data processing and model training is desirable.

Nice to have

• Experience in scientific machine learning, data assimilation, or hybrid physics-ML approaches.
• Background in leading cross-disciplinary research projects involving climate science, hydrology, and AI.
• Knowledge of deploying models into operational systems for real-world environmental applications.
• Experience with visualization tools and techniques for environmental data analysis.
• Familiarity with other programming languages or tools used in scientific computing, such as R or MATLAB, is advantageous.

Skills & tools

• Python programming language
• PyTorch or similar ML frameworks
• Large climate and environmental datasets (reanalysis, remote sensing, observational data)
• Data processing and workflow management tools (e.g., Jupyter, Git, Docker)
• Scientific writing and presentation skills
• Familiarity with cloud computing platforms, especially AWS, for data storage and model training

Practical notes

Location options include Cambridge, MA or Boulder, CO. Candidates based in Colorado may occasionally travel to the Cambridge headquarters for meetings or collaboration purposes. The salary range for this position is $127,000 to $205,900 for Colorado and $168,000 to $231,000 for Massachusetts, depending on experience and qualifications. Compensation will be determined based on the candidate's skills, experience, and overall fit for the role. Flagship Pioneering offers comprehensive healthcare coverage, an annual incentive program, retirement benefits, and a variety of other employee benefits designed to support work-life balance and professional development. The role involves working in a collaborative, innovative environment focused on solving complex environmental challenges through scientific excellence and technological innovation.