Description
About the Role
We're seeking an exceptional Data Science Architect who specializes in building intelligent decision-making systems that drive measurable business outcomes. This role sits at the intersection of advanced analytics, machine learning, and enterprise AI, partnering with cross-functional teams to transform complex business problems into scalable, data-driven solutions.
What You'll Do
Build Predictive Models & Decision Intelligence Systems
- Design and deploy end-to-end machine learning pipelines that predict business outcomes (e.g., renewal complexity, customer churn risk, revenue forecasting)
- Apply predictive modeling, causal inference, and optimization to create decision policies that maximize business KPIs
- Build ranking systems using advanced metrics (e.g., NDCG) to prioritize opportunities and optimize resource allocation
Drive Innovation in Agentic AI & Enterprise Intelligence
- Define the architectural relationship between Agentic AI systems and Decision Intelligence layers
- Design systems where AI agents execute tasks while Decision Intelligence systems determine which actions to take based on data-driven insights
- Bridge the gap between natural language understanding and quantifiable business impact
Partner with Business Teams
- Collaborate with renewal managers, sales operations, and customer success teams to understand business goals and translate them into analytical problems
- Present complex technical concepts to non-technical stakeholders through intuitive visualizations and explainable model outputs
- Iterate on solutions based on real-world feedback and changing business needs
Technical Excellence & Best Practices
- Work with Einstein Notebooks, Python, and enterprise data platforms to build production-grade ML solutions
- Troubleshoot complex data pipeline issues including S3 credential management and data access patterns
- Create comprehensive documentation including model cards, evaluation reports, and deployment guides
Required Skills & Experience
- 3+ years of experience in data science, machine learning, or related fields
- Proficiency in Python and ML frameworks (scikit-learn, XGBoost, LightGBM, etc.)
- Deep understanding of predictive modeling, classification, regression, and ranking algorithms
- Experience with model interpretability techniques (SHAP, LIME, interpretable boosting machines)
- Strong foundation in statistics, causal inference, and experimental design
- Proven track record of deploying ML models to production that drive measurable business value
AI/ML Tools & Technologies
- Languages: Python, SQL, R
- ML Frameworks: scikit-learn, XGBoost, pandas, numpy
- Platforms: Einstein Notebooks, Jupyter, Databricks, AWS/S3
- Evaluation Metrics: NDCG, AUC-ROC, precision-recall, custom ranking metrics
- Model Types: Gradient boosting, ensemble methods, interpretable ML models
- Data Engineering: ETL pipelines, feature engineering, data quality validation
Desirable Experience
- Background in Large Language Models (LLMs) and generative AI, including prompt engineering and understanding LLM capabilities versus traditional ML
- Experience with Salesforce products (CRM, Marketing Cloud, Tableau) and enterprise data structures
- Knowledge of optimization algorithms and operations research techniques
- Experience with A/B testing and experimentation frameworks
- Publications or presentations in data science/ML communities
For roles in San Francisco and Los Angeles: Pursuant to the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, Salesforce will consider for employment qualified applicants with arrest and conviction records.