Senior Data Scientist III - Machine Learning for GEOINT and Remote Sensing

Overview

At PNNL, our core capabilities are divided among major departments that we refer to as Directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget.

Our Science & Technology directorates include National Security, Integrated Discovery Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy, Office of Science user facility housed on the PNNL campus.

The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real-world threats to our nation and the world.

The AI and Data Analytics Division, part of the National Security Directorate, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster. Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

Responsibilities

PNNL is seeking a Senior Data Scientist III with deep learning expertise to develop methods for detecting and classifying targets in electro-optical (EO) imagery or identifying targets and materials in hyperspectral imagery (HSI). This research supports geospatial intelligence (GEOINT) and national missions.

In the AI and Data Analytics Division, you will lead research projects or major parts of larger projects, developing methods from initial ideas through evaluation and integration into research prototypes.

Current priorities include automatic target recognition (ATR), long-wave infrared (LWIR) HSI target detection and material classification, and detection and classification with limited labeled training examples, particularly in EO imagery. The role also offers opportunities to explore related problems in remote sensing and intelligence analysis. Candidates with deep learning experience in EO, HSI, or related sensing applications are encouraged to apply.

You will work alongside researchers and domain specialists on difficult national science and technology challenges. Collaboration with software and machine learning (ML) engineers will reflect each project's needs and stage of development. Opportunities to advance existing research and pursue new ideas may come through ongoing projects, internal research investments, or new proposals.

Key Responsibilities

• Develop and adapt detection and classification methods through algorithm development, mathematical analysis, experimentation, or implementation.
• Lead research projects or major technical efforts, coordinate contributors, and plan the work, resources, and milestones.
• Evaluate methods against appropriate baselines using representative data. Investigate classification errors, false alarms, robustness, and limitations that affect their use.
• Build and validate research prototypes with software and ML engineers, addressing technical barriers to integration and deployment.
• Work with collaborators to turn mission or user needs into research questions, and communicate findings through analyses, technical reports, and discussions.
• Develop research ideas and collaborate with project leaders on proposals to support your research.
• Mentor colleagues, help recruit and onboard researchers, and share methods and findings across disciplines.
• Follow applicable quality, safety, and security requirements.

This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.

Qualifications

Minimum Qualifications:

• BS/BA and 5+ years of data science experience -OR-
• MS/MA and 3+ years of data science experience -OR-
• PhD with 1+ year of data science experience
• Experience applying and evaluating deep learning methods for detection and classification in electro-optical (EO) imagery or target detection and material classification in hyperspectral imagery (HSI).
• Experience developing deep learning methods using PyTorch or another deep learning framework. 

Preferred Qualifications:

• Experience with the EO or HSI imaging chain, including sensor response, image formation, calibration, and data processing. LWIR HSI experience is particularly valued.
• Experience in addressing limited labeled training data or new target categories through methods such as few-shot learning, transfer learning, or open-vocabulary models.
• Experience with multimodal or temporal fusion for geospatial analysis, or agentic workflows that coordinate models, geospatial tools, and data sources, including evaluation of workflow reliability and analytic results.
• Experience translating research into methods or tools used in applied settings, or developing and evaluating models for edge or onboard platforms with size, weight, and power (SWaP) constraints, balancing accuracy, latency, memory, and energy use.
• Experience supporting national security missions or developing proposals.

Hazardous Working Conditions/Environment

Not applicable.

Additional Information

This position requires the ability to obtain and maintain a federal security clearance.

A security clearance background investigation includes review of your employment, education, financial, and criminal history, as well as interviews with you and your personal references, neighbors, and co-workers to determine trustworthiness, reliability, and loyalty to the United States.  The investigation also examines your foreign connections, drug and alcohol use, foreign influence, and overall conduct.

Requirements:

• U.S. Citizenship
• Background Investigation: Applicants selected will be subject to a Federal background investigation and must meet eligibility requirements for access to classified matter in accordance with 10 CFR 710, Appendix B.
• Drug Testing: All Security Clearance positions are Testing Designated Positions, which means that the applicant selected for hire is subject to pre-employment drug testing, and post-employment random drug testing. In addition, applicants must be able to demonstrate non-use of illegal drugs, including marijuana, for the 12 consecutive months preceding completion of the requisite Questionnaire for National Security Positions (QNSP).

Note: Applicants will be considered ineligible for security clearance processing by the U.S. Department of Energy if non-use of illegal drugs, including marijuana, for 12 months cannot be demonstrated.

Testing Designated Position

This position is a Testing Designated Position (TDP). The candidate selected for this position will be subject to pre-employment and random drug testing for illegal drugs, including marijuana, consistent with the Controlled Substances Act and the PNNL Workplace Substance Abuse Program.

About PNNL

Pacific Northwest National Laboratory (PNNL) is a world-class research institution powered by a highly educated, diverse workforce committed to the values of Integrity, Creativity, Collaboration, Impact, and Courage. Every year, scores of dynamic, driven people come to PNNL to work with renowned researchers on meaningful science, innovations and outcomes for the U.S. Department of Energy and other sponsors; here is your chance to be one of them!

At PNNL, you will find an exciting research environment and excellent benefits including health insurance, and flexible work schedules. PNNL is located in eastern Washington State—the dry side of Washington known for its stellar outdoor recreation and affordable cost of living. The Lab’s campus is only a 45-minute flight (or ~3 hour drive) from Seattle or Portland, and is serviced by the convenient PSC airport, connected to 8 major hubs.

Commitment to Excellence and Equal Employment Opportunity

Our laboratory is committed to fostering a work environment where all individuals are treated with fairness and respect while solving critical challenges in fundamental sciences, national security, and energy resiliency. We are an Equal Employment Opportunity employer.

Pacific Northwest National Laboratory (PNNL) is an Equal Opportunity Employer. PNNL considers all applicants for employment without regard to race, religion, color, sex, national origin, age, disability, genetic information (including family medical history), protected veteran status, and any other status or characteristic protected by federal, state, and/or local laws.

Drug Free Workplace

PNNL is committed to a drug-free workplace supported by Workplace Substance Abuse Program (WSAP) and complies with federal laws prohibiting the possession and use of illegal drugs.

If you are offered employment at PNNL, you must pass a drug test prior to commencing employment. PNNL complies with federal law regarding illegal drug use. Under federal law, marijuana remains an illegal drug.  If you test positive for any illegal controlled substance, including marijuana, your offer of employment will be withdrawn.

Security, Credentialing, and Eligibility Requirements

As a national laboratory, PNNL is responsible for adhering to the Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which require new employees to obtain and maintain a HSPD-12 Personal Identify Verification (PIV) Credential. To obtain this credential, new employees must successfully complete the applicable tier of federal background investigation post hire and receive a favorable federal adjudication.  The tier of federal background investigation will be determined by job duties and national security or public trust responsibilities associated with the job. All tiers of investigation include a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last 1 to 7 years (depending on the applicable tier of investigation). Illegal drug activities include marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) Federal risk determination to maintain employment. Once you meet the three-year residency requirement thereafter, you will be required to obtain a PIV credential to maintain employment. The tier of federal background investigation required to obtain the PIV credential will be determined by job duties at the time you become eligible for the PIV credential.

Mandatory Requirements

Please be aware that the Department of Energy (DOE) prohibits DOE employees and contractors from having any affiliation with the foreign government of a country DOE has identified as a “country of risk” without explicit approval by DOE and Battelle. If you are offered a position at PNNL and currently have any affiliation with the government of one of these countries, you will be required to disclose this information and recuse yourself of that affiliation or receive approval from DOE and Battelle prior to your first day of employment.

Rockstar Rewards

Employees and their families are offered medical insurance, dental insurance, vision insurance, robust telehealth care options, several mental health benefits, free wellness coaching, health savings account, flexible spending accounts, basic life insurance, disability insurance*, employee assistance program, business travel insurance, tuition assistance, relocation, backup childcare, legal benefits, supplemental parental bonding leave, surrogacy and adoption assistance, and fertility support. Employees are automatically enrolled in our company-funded pension plan* and may enroll in our 401 (k) savings plan with company match*. Employees may accrue up to 120 vacation hours per year and may receive ten paid holidays per year.

* Research Associates excluded.

**All benefits are dependent upon eligibility.

Click Here For Rockstar Rewards

Notice to Applicants

PNNL lists the full pay range for the position in the job posting. Starting pay is calculated from the minimum of the pay range and actual placement in the range is determined based on an individual’s relevant job-related skills, qualifications, and experience. This approach is applicable to all positions, with the exception of positions governed by collective bargaining agreements and certain limited-term positions which have specific pay rules.

As part of our commitment to fair compensation practices, we do not ask for or consider current or past salaries in making compensation offers at hire. Instead, our compensation offers are determined by the specific requirements of the position, prevailing market trends, applicable collective bargaining agreements, pay equity for the position type, and individual qualifications and skills relevant to the performance of the position.

Minimum Salary
USD $140,200.00/Yr.
Maximum Salary
USD $228,800.00/Yr.

PNNL is seeking a Senior Data Scientist III with deep learning expertise to develop methods for detecting and classifying targets in electro-optical (EO) imagery or identifying targets and materials in hyperspectral imagery (HSI). This research supports geospatial intelligence (GEOINT) and national missions.

In the AI and Data Analytics Division, you will lead research projects or major parts of larger projects, developing methods from initial ideas through evaluation and integration into research prototypes.

Current priorities include automatic target recognition (ATR), long-wave infrared (LWIR) HSI target detection and material classification, and detection and classification with limited labeled training examples, particularly in EO imagery. The role also offers opportunities to explore related problems in remote sensing and intelligence analysis. Candidates with deep learning experience in EO, HSI, or related sensing applications are encouraged to apply.

You will work alongside researchers and domain specialists on difficult national science and technology challenges. Collaboration with software and machine learning (ML) engineers will reflect each project's needs and stage of development. Opportunities to advance existing research and pursue new ideas may come through ongoing projects, internal research investments, or new proposals.

Key Responsibilities

• Develop and adapt detection and classification methods through algorithm development, mathematical analysis, experimentation, or implementation.
• Lead research projects or major technical efforts, coordinate contributors, and plan the work, resources, and milestones.
• Evaluate methods against appropriate baselines using representative data. Investigate classification errors, false alarms, robustness, and limitations that affect their use.
• Build and validate research prototypes with software and ML engineers, addressing technical barriers to integration and deployment.
• Work with collaborators to turn mission or user needs into research questions, and communicate findings through analyses, technical reports, and discussions.
• Develop research ideas and collaborate with project leaders on proposals to support your research.
• Mentor colleagues, help recruit and onboard researchers, and share methods and findings across disciplines.
• Follow applicable quality, safety, and security requirements.

This position is based in either Richland, WA or Seattle, WA and requires an onsite presence.

Minimum Qualifications:

• BS/BA and 5+ years of data science experience -OR-
• MS/MA and 3+ years of data science experience -OR-
• PhD with 1+ year of data science experience
• Experience applying and evaluating deep learning methods for detection and classification in electro-optical (EO) imagery or target detection and material classification in hyperspectral imagery (HSI).
• Experience developing deep learning methods using PyTorch or another deep learning framework. 

Preferred Qualifications:

• Experience with the EO or HSI imaging chain, including sensor response, image formation, calibration, and data processing. LWIR HSI experience is particularly valued.
• Experience in addressing limited labeled training data or new target categories through methods such as few-shot learning, transfer learning, or open-vocabulary models.
• Experience with multimodal or temporal fusion for geospatial analysis, or agentic workflows that coordinate models, geospatial tools, and data sources, including evaluation of workflow reliability and analytic results.
• Experience translating research into methods or tools used in applied settings, or developing and evaluating models for edge or onboard platforms with size, weight, and power (SWaP) constraints, balancing accuracy, latency, memory, and energy use.
• Experience supporting national security missions or developing proposals.