Senior Data Scientist – Applied AI

Salesforce•Palo Alto, CA

1d•$148,500 - $285,800•Remote

About The Position

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Our Data Science team builds the next generation of enterprise AI systems powering conversational agents, voice experiences, language models, and intelligent automation. We work across the entire AI stack, from data and model development to evaluation, safety, and production optimization, to deliver reliable, trustworthy AI at enterprise scale. We're looking for a Senior Data Scientist who is passionate about applying machine learning, statistics, and generative AI to solve complex real world problems. You'll work closely with engineers, product managers, researchers, and business stakeholders to build, optimize, and evaluate AI systems that deliver measurable customer impact.

Requirements

5+ years of experience in Data Science, Machine Learning, Applied AI, or a related technical field
Strong experience with Large Language Models (LLMs), Generative AI, and AI agents
Experience with prompt engineering, context engineering, and Retrieval-Augmented Generation (RAG)
Experience fine tuning and adapting foundation models for domain specific applications
Experience applying reinforcement learning and modern model optimization techniques
Strong foundation in machine learning, statistics, experimentation, and causal inference
Experience designing A/B tests and interpreting experimental results
Proficiency in Python and common machine learning frameworks
Experience monitoring and continuously improving machine learning models in production
Excellent communication and collaboration skills, with the ability to influence cross functional teams and executive stakeholders

Nice To Haves

Master's or Ph.D. in Computer Science, Data Science, Statistics, Mathematics, Operations Research, Machine Learning, or a related quantitative field, or equivalent practical experience
Experience with speech, multimodal AI, conversational AI, or voice agents is a plus

Responsibilities

Design and execute experiments to evaluate and improve the quality of large language models (LLMs), voice/text AI systems, multimodal models, and long horizon task agents
Build scalable evaluation datasets, benchmarks, and automated evaluation frameworks across language, speech, reasoning, and agent workflows
Analyze large-scale product, customer, and model telemetry to identify failure modes, performance bottlenecks, and opportunities for improvement
Develop statistical models, predictive analytics, and experimentation frameworks to measure model quality, user experience, and business impact
Develop, optimize, and evaluate prompts, system instructions, retrieval strategies, and context engineering techniques to improve performance, reliability, efficiency, and safety of AI applications
Fine-tune and adapt foundation models to improve task specific performance, efficiency, and enterprise readiness using supervised learning, reinforcement learning, and other modern techniques
Design and curate high quality datasets for model training, evaluation, and continuous improvement of AI systems
Partner with cross functional stakeholders to define AI product requirements, success metrics, experimentation strategies, and data driven roadmaps
Build dashboards, analytics pipelines, and reporting frameworks that provide actionable insights into AI quality, reliability, customer experience, and business outcomes
Apply statistical inference, causal analysis, and machine learning techniques to solve challenging product and operational problems
Develop scalable evaluation methodologies for Responsible AI, including safety, robustness, fairness, security, and governance
Communicate technical findings and recommendations clearly to both technical and executive audiences