Machine Learning Research Scientist – Atmospheric Sciences

Job not on LinkedIn

🔥 13 hours ago

🗽 New York – Remote

🗽 New York – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🧬 Research Scientist

👻 Ghost score 10%

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SAIC

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1969

☁️ SaaS

📣 Marketing

🏢 Enterprise

🔥 Funding within the last year

💰 $500M Post-IPO Debt - SAIC on 2025-09

SaaS • Marketing • Enterprise

SAIC is represented in the supplied content by marketing and product copy that matches Bitly — a cloud-based link management and QR-code platform. The material describes a SaaS offering for URL shortening, dynamic QR Codes, link analytics, mobile-friendly landing pages, branded links, UTM campaign tracking, digital business cards, developer APIs and AI-enhanced features. It targets marketers, developers, and enterprises across industries (retail, CPG, hospitality, media, healthcare, finance, education) and emphasizes integrations, analytics, and enterprise-grade capabilities. Note: the provided text appears to be Bitly’s website content rather than information about SAIC; if you intended the defense/technology firm SAIC (Science Applications International Corporation), please provide relevant content.

📋 Description

• Provide technical support services applying machine learning to Navy Numerical Weather Prediction development and testing • Evaluate emerging high-resolution AI weather prediction systems for Navy-relevant maritime environments • Develop a high-resolution, multi-year COAMPS-based regional weather reanalysis dataset over the Western Pacific • Fine-tune and transition a GraphCast-based global machine learning weather model into FNMOC operations • Evaluate additional emerging machine learning weather models • Coordinate development of a lightweight regional machine learning model to improve maritime decision-making and forecasting • Investigate physical processes, air-sea fluxes, and boundary-layer influences affecting multiscale predictability of atmospheric rivers • Use Navy prediction systems, machine learning models, and targeted field observations such as dropsondes and buoys to improve high-impact weather forecasting operational suitability • Perform work in accordance with DSRC usage policies, security requirements, and Government-furnished technical direction

🎯 Requirements

• Bachelor's degree and two or more years of experience; Master's degree and 0 years related experience; or PhD • PhD in Atmospheric Sciences or a related scientific or engineering field of study • Minimum of 3 years of demonstrated research experience in applying machine learning models to probabilistic weather forecasting • Experience evaluating forecast skill and systematic biases in NWP reforecasts • Experience improving decision-making under identified regime-dependent uncertainty • Experience evaluating AI climate emulators to reproduce and forecast stratosphere-troposphere coupling • Programming proficiency in Python, R, and SQL • Experience with a combination of AWS, Git/GitHub, high-performance computing (HPC), GPU-accelerated computing, ERA5/ERA-I, ECMWF and UFS reforecast, and AI-based climate/weather emulators including GraphCast and ACE2 • Completed T3 Security Investigation required for full access to government IT systems • Desired: experience publishing peer-reviewed scientific literature, software documentation, guidebooks, and/or handbooks • Desired: high-performance computing experience

🏖️ Benefits

• Remote work arrangement • Equal Opportunity Employer