Jr Scientist

Stcnet; College Park (MD); On-site; USD 60,000 - 90,000; Full time

Benefits offered by this job

Paid Time Off

401K with employer match

Flexible spending account

Health savings account

Job summary

STC seeks a Jr Scientist to support GNSS radio occultation data processing and retrievals for atmospheric sciences. You will drive validation against reference datasets and contribute to operational software and reports.

The role requires deep domain knowledge in RO techniques, experience with Python/Fortran, and familiarity with cloud-based workflows for long-term production readiness.

Qualifications

• PhD or MS in Atmospheric Science, Remote Sensing, or related field with GNSS RO focus.

Responsibilities

• Validate and operationalize next-generation satellite atmospheric retrievals.
• Lead evaluation of physical and AI/ML inversion algorithms.
• Prepare baseline code, ATBDs, and evaluation reports.
• Deliver OCS-compliant operational package in 1–2 years.
• Compare retrievals against independent datasets (radiosondes, dropsondes).
• Assess retrieval performance across clouds, land/ocean, inversions, aerosols.

Skills

GNSS RO knowledge

Python (NumPy/SciPy/xarray)

Fortran/C++

Cloud computing

Education

Master's Degree in Atmospheric Science/Remote Sensing or related

PhD preferred in Atmospheric Science

Tools

MATLAB

NCWCP College Park infrastructure

Job description

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Job Title

Jr Scientist

Location

COLLEGE PARK, MD, MD 20740 US (Primary)

Category

Scientist

Job Type

Full-Time

$60,000

$90,000

Education

Master's Degree

Security Clearance Required

None

Job Description

• Employment Category:Full-Time/Regular
• Location:NCWCP College Park, MD
• Travel: Some travel may be required both locally and domestically by car or plane.
• Education: PhD or MS in Atmospheric Science, Remote Sensing, or a related field with a strong focus on GNSS radio occultation data processing.
• Security Clearance: None
• Salary: Depending on Experience

Job Description

STC supports NOAA NESDIS’s Center for Satellite Applications and Research (STAR) by providing scientific, engineering, and programmatic expertise across satellite algorithm development, calibration/validation, data product generation, and technology transition. Our team helps NOAA accelerate the delivery of high-quality environmental data products from current and next-generation satellite systems to users worldwide.

STC is seeking a highly analytical Remote Sensing Data Scientist to support the following support activities for Atmospheric Sciences & Technology Applications (ASTA 2.0) Team to:

• Drive the validation and operationalization of next-generation satellite atmospheric retrievals. This role centers on the GXS T/q Level-2 validation program, utilizing Global Navigation Satellite System Radio Occultation (GNSS-RO) measurements as a critical reference dataset.
• Lead the rigorous assessment of both physical and AI/ML inversion algorithms. By adhering to a strict, multi-year evaluation schedule and utilizing MTG-IRS as a pre-launch proxy, this scientist will ensure that all final algorithm selections meet the strict latency, accuracy, and format requirements for numerical weather prediction and operational deployment.
• Validation Schedule Execution: Execute formal remote sensing algorithm (NUCAPS, MIIDAPS-AI) evaluation cycles, including Round 1 retrievals
• Operational Delivery: Prepare baseline code, Algorithm Theoretical Basis Documents (ATBD), and evaluation reports. Finalize and deliver the ultimate OCS-compliant operational package in 1-2 years.
• Project Implementation: Collect one year of NESDIS STAR Retrieval Algorithms (NUCAPS, MIIDAPS-AI) data, and RO data. Check the quality of the RO data and quantify all retrievals using independent datasets. Utilize RO and all available datasets to guide the selection of the best inversion algorithms for GXS by employing MTG/IRS as a proxy.
• Retrieval Accuracy Assessment: Calculate algorithm bias, RMSE, and vertical structure against independent reference data, including radiosondes, dropsondes, surface observations, and reanalysis models.
• Robustness & Yield Evaluation: Analyze error variance, convergence/failure modes, and retrieval performance across variable conditions such as clouds, land/ocean boundaries, inversions, and aerosol/dust events. Monitor the usable single-FOV retrieval fraction and spatial/temporal representativeness.
• Latency & Efficiency Scoring: Benchmark competing retrieval candidates on wall-clock speed, memory/compute demand, data-flow latency, and near-real-time feasibility.
• NWS / DA Utility Verification: Ensure all variables, atmospheric levels, metadata, and formats fully support operational forecasters and numerical weather prediction (NWP) assimilation requirements.
• Paid Time Off Starting at 80 hrs/yr, 11 Federal holidays, and 40 hrs/yr Sick Leave
• 401K with up to 4% employer matching contribution
• Flexible spending account
• Health savings account

Job Requirements

• Domain Expertise: Deep understanding of the physical principles of GNSS Radio Occultation and hyperspectral infrared sounders (e.g., CrIS, MTG-IRS).
• Algorithm Familiarity: Experience evaluating physical baseline retrieval methods (such as those utilizing SARTA or PCRTM) and AI/ML approaches (including U-NET or Transformers).
• Programming Skills: Proficiency in Python (NumPy, SciPy, xarray) and/or Fortran/C++ for processing large satellite datasets and building automated scoring scripts.
• Data Handling: Extensive experience working with Level-2 retrieval datasets, common product formats, and applying standardized quality control (QC) definitions.
• Evaluation Governance: Strong ability to operate within a strict governance framework, utilizing frozen evaluation criteria, test cases, and version-controlled submissions. The ability to develop and maintain science applications in cloud-based operating environments is required in support of the NOAA-NESDIS 5-year plan to migrate to a cloud-based production and development environment.

Preferred Qualifications

• Cloud Computing: Experience operating data processing pipelines in cloud environments (AWS, Google Cloud, or Azure) to manage high-volume commercial and government satellite data.
• Algorithm Heritage: Familiarity with NUCAPS, MIIDAPS-AI, and retrieving atmospheric profiles under all-sky conditions using AI models trained on FV3GFS or ERA-5 data.
• Operational Transition Readiness: Proven track record of evaluating code structure, dependencies, and calibration sensitivity to assess a scientific algorithm's readiness for long-term operational maintainability.

We are equal opportunity/affirmative action employers, committed to diversity in the workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, disability, or protected veteran status, or any other protected characteristic under state or local law.