Manager, Strategic Data Science

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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.

• Role Description: As a Strategic Data Scientist, you will own the end-to-end design, development, and production deployment of advanced AI and data-driven solutions. You’ll build scalable machine-learning models with large, heterogeneous datasets to solve complex business challenges and provide proactive, data-driven guidance to our Customer Success organization.
  
  Key Responsibilities:

  • 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 (classification, regression, NLP, recommendation engines, etc.) to identify at-risk customers, predict attrition, and assess impact of product offerings

  • Develop customized recommendation engines that suggest next-best actions for customers (collaborative filtering, content-based, hybrid, graph-based techniques, etc.)

  • Drive the end-to-end machine learning lifecycle, from data preprocessing and feature engineering to model training, testing, and automated retraining workflows

  • Architect high-performance data pipeline for massive, multi-source datasets (streaming, batch, semi-structured), ensuring optimal storage, fast query performance, and high data integrity in hybrid cloud environments

  • Monitor production model performance by tracking key metrics like accuracy, drift, and latency. Leverage A/B testing and establish feedback loops to drive continuous improvement and rapid iteration

  • Support translation of strategic direction into analytical problems and actionable data science initiatives, ensuring data science alignment with organizational goals and long-term vision

  • Present clear, actionable insights and technical roadmaps to technical and non-technical stakeholders at all levels
     

  Collaborative Partners:

  • Customer Success Leadership: define priority use cases and success metrics for AI-driven initiatives

  • Product & Engineering: embed data-science solutions into product features and roadmaps

  • Data Platform & MLOps: utilize internal infrastructure for data access, orchestration, and scalable deployments

  • Business Operations & Finance: validate model assumptions, quantify ROI, and support strategic planning
     

  Role Requirements:

  • Education: Bachelor’s or Master’s in quantitative field such asData Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline

  • Experience: 2–5 years of hands-on experience building and deploying machine-learning solutions—especially recommender systems—in a SaaS or customer-facing environment

  • Technical Proficiency: Proficient in Python (or R) and ML frameworks (scikit-learn, TensorFlow, PyTorch); expertise with data tools (SQL, Spark, Airflow) and cloud platforms (AWS, GCP, Azure)

  • AI & Next-Gen Models: Demonstrated experience with embedding techniques, transformer-based models, and graph ML for large-scale recommendations

  • Business Acumen: Strong analytical mindset; able to translate model outputs into clear business recommendations and track impact through defined KPIs

  • Communication & Influence: Excellent at distilling complex technical concepts for non-technical audiences and driving alignment across teams

  • Self-Starter: Thrives in ambiguous environments; owns projects end-to-end and iterates based on feedback
     

  Preferred Qualifications:

  • Enterprise-Scale Recommenders: Previous hands-on experience building and scaling recommender systems at major technology platforms

  • Top-Tier Consulting Background: Prior experience at a leading strategy firm with demonstrated