Senior Data Scientist

About Figure

Figure Technology Solutions, Inc. (Nasdaq: FIGR; OPEN: FGRS) is the leading blockchain-native capital marketplace for the origination, funding, sale and trading of tokenized assets. More than 480 partners use its loan origination system and capital marketplace. Collectively, Figure and its partners have originated over $30 billion of loans to date, among other products. The fastest growing components are Figure Connect, its credit marketplace, and Democratized Prime, Figure’s on-chain lend-borrow marketplace. Figure’s ecosystem also includes DART (Digital Asset Registry Technology) for asset custody and lien perfection, and $YLDS, a yield-bearing stablecoin registered with the SEC as a security under the Securities Act of 1933.

Figure is the market leader in real-world asset (RWA) tokenization. The company has received triple-A ratings ratings from S&P and Moody’s on multiple loan securitizations, the first of its kind for blockchain finance.

We’re proud to be recognized as one of Forbes’ Top 50 Fintech Companies and CNBC’s World’s Top Fintech Companies.

About the Role

We’re looking for a Senior Data Scientist to join our growing team! As Senior Data Scientist, you’ll develop and apply modern machine learning and artificial intelligence across a range of products and business problems. You’ll combine strong computer science foundations, rigorous experimental judgment, and the ability to turn advances in AI into reliable systems with measurable business impact. The work spans document intelligence, multimodal systems that combine text, images, and structured data, and the adaptation of foundation models using proprietary datasets. These models support complex workflows in regulated financial services, where accuracy, explainability, data governance, reproducibility, and careful evaluation are essential. You’ll work across the full machine learning lifecycle and turn successful approaches into reusable AI capabilities, patterns, and components that support multiple teams rather than building a one-off solution for every problem.

What You’ll Do

• Identify high-impact opportunities for machine learning and AI in partnership with product, engineering, operations, and business teams
• Design, train, fine-tune, and evaluate machine learning and deep-learning models
• Work with foundation models across language, vision, and multimodal applications
• Develop robust datasets, features, training pipelines, and evaluation frameworks
• Run efficient experiments on GPU infrastructure and improve model quality, speed, and cost
• Compare alternative modeling approaches and make clear recommendations based on empirical results and business impact
• Build reusable modeling components, evaluation frameworks, and implementation patterns that accelerate work across teams and projects
• Establish clear interfaces and standards that allow other teams to adopt proven AI capabilities without rebuilding the underlying modeling work
• Build models and supporting systems that are reproducible, testable, and suitable for production use
• Partner with ML and software engineers on deployment, monitoring, reliability, and ongoing model improvement
• Communicate results, limitations, and tradeoffs clearly to both technical and non-technical stakeholders
• Stay current with advances in machine learning and determine when emerging techniques are mature enough to create practical value
• Turn ambiguous business needs into well-defined machine learning problems and measurable success criteria
• Build models that improve real customer or operational outcomes, not only offline metrics
• Make thoughtful tradeoffs among model quality, complexity, latency, reliability, and cost
• Create reusable foundations that allow multiple teams to apply proven AI capabilities to new problems more quickly and consistently
• Help raise the organization’s technical standards for experimentation, evaluation, and production AI

What We Look For

• Bachelor’s or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience