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.

Build Predictive Models & Decision Intelligence Systems

• Design and deploy end-to-end machine learning pipelines that predict business outcomes (eg, 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 (eg, 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