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. We are 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. As a Strategic Data Scientist, you will own the end-to-end design, development, and production deployment of advanced AI and data-driven solutions, building scalable machine-learning models with large, heterogeneous datasets to solve complex business challenges and provide proactive, data-driven guidance to our Customer Success organization.

You will collaborate with customer success, product, engineering, and sales teams to define KPIs and analytical approaches that answer key business questions, design, build, and deploy machine learning and AI models—including classification, regression, NLP, and recommendation engines—to identify at-risk customers, predict attrition, and assess the impact of product offerings, develop customized recommendation engines suggesting next-best actions for customers using collaborative filtering, content-based, hybrid, or graph-based techniques, and drive the end-to-end machine learning lifecycle from data preprocessing and feature engineering to model training, testing, and automated retraining workflows. You will architect high-performance data pipelines for massive, multi-source datasets, monitor production model performance via accuracy, drift, and latency metrics, leverage A/B testing and feedback loops, translate strategic direction into actionable data science initiatives, and present clear insights and technical roadmaps to stakeholders at all levels.

Collaborative partners include Customer Success Leadership to define priority use cases and success metrics, Product & Engineering to embed data-science solutions into features and roadmaps, Data Platform & MLOps for data access, orchestration, and scalable deployments, and Business Operations & Finance to validate model assumptions, quantify ROI, and support strategic planning.

Required qualifications include a Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field, with 2–5 years of hands-on experience building and deploying machine-learning solutions, especially recommender systems, in SaaS or customer-facing environments. You must be proficient in Python or R, ML frameworks such as scikit-learn, TensorFlow, or PyTorch, and data tools like SQL, Spark, and Airflow, with experience using cloud platforms including AWS, GCP, or Azure. Candidates should demonstrate expertise with embedding techniques, transformer-based models, and graph ML for large-scale recommendations, possess strong analytical and business acumen, and have excellent communication skills to translate complex technical concepts for non-technical audiences. Self-starters who thrive in ambiguous environments and own projects end-to-end are ideal.

Preferred qualifications include prior hands-on experience building and scaling enterprise recommender systems, experience at a top-tier consulting firm, LLM proficiency for data augmentation, prompt engineering, or analytics automation, and advanced AI use cases applying transformer fine-tuning, embedding retrieval, and graph neural networks for production recommender or decision-support systems.

Unleash your potential at Salesforce with comprehensive benefits including time off, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and employee stock purchasing programs. Base salary range for New York-based roles is $138,800 to $233,200. Salesforce is an equal opportunity employer committed to inclusive, non-discriminatory practices, and reasonable accommodations are available for applicants with disabilities.