Strategic Data Science Manager, Salesforce

• 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

Desirable

• 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
• 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
• 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