Senior Data Scientist

Role overview

Build machine learning and AI capabilities for complex product and operational needs in financial services. The role covers the full modeling lifecycle, including document intelligence, language and vision systems, multimodal data, and foundation model adaptation, with careful attention to evaluation, reliability, governance, and measurable outcomes.

Responsibilities

- Identify valuable ML and AI opportunities with product, engineering, operations, and business partners. - Design, train, fine-tune, and evaluate machine learning and deep-learning models, including foundation models for language, vision, and multimodal use cases. - Develop datasets, features, training pipelines, and evaluation methods; run GPU experiments to improve quality, speed, and cost. - Compare modeling options and recommend approaches using empirical evidence and business impact. - Create reusable components, evaluation frameworks, interfaces, and standards that other teams can adopt. - Partner with engineering on production deployment, monitoring, reliability, and ongoing improvement; communicate results and limitations to technical and non-technical audiences.

Requirements

- Bachelor’s or advanced degree in computer science, machine learning, electrical engineering, applied mathematics, or a related field, or equivalent practical experience. - At least five years developing machine learning solutions, including taking models beyond initial experiments. - Ability to own open-ended problems from investigation through implementation and measurement. - Ability to turn business needs into defined ML problems and success measures. - Strong communication and cross-functional collaboration skills. - Sound judgment about tradeoffs among model quality, complexity, latency, reliability, and cost.

Nice to have

- Master’s degree or PhD in a relevant technical field. - Experience with distributed training, model optimization, quantization, or parameter-efficient fine-tuning. - Experience deploying and monitoring production ML systems, especially in regulated or high-stakes settings. - Familiarity with cloud infrastructure, distributed data processing, and modern ML platforms. - Contributions to applied research, open-source work, patents, or production AI products.

Benefits and work setup

- The listing indicates a hybrid work arrangement and a U.S. base salary range of $132,000–$198,000 per year, a 25% annual bonus target, and equity. - Benefits include medical, dental, and vision coverage; savings and retirement plans; commuter benefits; life and disability coverage; paid holidays and time off; up to 12 weeks of paid family leave; and education reimbursement. - Work visa sponsorship is not available.