Senior Data Scientist – Applied AI

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Job Category

Software Engineering

Job Details

About Salesforce

Salesforce is the #1 AI CRM, where humans with agents drive customer success together. Here, ambition meets action. Tech meets trust. And innovation isn’t a buzzword — it’s a way of life. The world of work as we know it is changing and we're looking for Trailblazers who are passionate about bettering business and the world through AI, driving innovation, and keeping Salesforce's core values at the heart of it all.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

About the Team
 

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.

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

Preferred Qualifications

• 5+ years of experience in Data Science, Machine Learning, Applied AI, or a related technical field
• 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
• 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 with speech, multimodal AI, conversational AI, or voice agents is a plus
• 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

Preferred Skills

• Large Language Models (LLMs)