Senior Data Scientist

Principal Data Scientist, AI Foundations Location: San Jose, California, United States (On-site) Industry: Financial Services Work Setting: On-site Are you passionate about building next-generation AI solutions, advancing machine learning innovation, and transforming massive datasets into intelligent products that create exceptional customer experiences?

Join a cutting-edge AI team where you'll develop state-of-the-art machine learning and generative AI solutions, collaborate with cross-functional experts, and deliver scalable models that power impactful digital experiences for millions of users.

Key Responsibilities

• Partner with data scientists, machine learning engineers, software engineers, product managers, and business stakeholders to develop AI-driven products and experiences.
• Design, develop, train, evaluate, and deploy advanced machine learning, deep learning, and natural language processing models.
• Leverage modern AI and cloud technologies, including large-scale computing environments, open-source frameworks, vector databases, and generative AI ecosystems.
• Apply expertise in Natural Language Processing (NLP) and Large Language Models (LLMs) to develop innovative customer-facing applications.
• Fine-tune, optimize, and evaluate foundation models and generative AI solutions for production use cases.
• Build scalable AI systems capable of processing large volumes of structured and unstructured data.
• Collaborate with engineering teams to operationalize models in resilient, high-performance production environments.
• Translate complex analytical findings and technical concepts into actionable business recommendations and strategic insights.
• Research, evaluate, and apply emerging AI, machine learning, and generative AI technologies to solve challenging business problems.
• Drive innovation through experimentation, rapid prototyping, and continuous improvement of AI capabilities.
• Mentor team members, influence technical direction, and contribute to the growth of AI and data science best practices.

Required Qualifications

• Bachelor's, Master's, or PhD degree in Statistics, Mathematics, Computer Science, Analytics, Operations Research, Economics, Engineering, or another quantitative discipline.
• Professional experience in data analytics, data science, machine learning, or a related analytical field.
• Strong foundation in statistical analysis, predictive modeling, machine learning, and data-driven decision-making.
• Experience building and deploying machine learning models in production environments.
• Strong programming and problem-solving skills with the ability to work across the full model development lifecycle.
• Excellent communication skills with the ability to explain complex technical concepts to both technical and non-technical audiences.

Preferred Qualifications

• Advanced degree (Master's or PhD) in a STEM-related field.
• Experience working with cloud platforms and distributed computing environments.
• Proficiency in Python, Scala, R, or similar programming languages.
• Strong experience with machine learning, deep learning, and artificial intelligence frameworks.
• Experience with SQL and large-scale data processing technologies.
• Hands-on experience with Large Language Models (LLMs), Generative AI, Retrieval-Augmented Generation (RAG), prompt engineering, model fine-tuning, or foundation models.
• Expertise in one or more advanced AI domains such as NLP, computer vision, reinforcement learning, explainable AI, training optimization, self-supervised learning, or human feedback alignment techniques.
• Experience developing scalable AI platforms, reusable libraries, frameworks, or enterprise AI solutions