Data Science Architect - AI​/ML & Decision Intelligence

Overview

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Job Category:
Operations

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.

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.

• 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.

• 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.

• 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.

• 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.

• 3+ years of experience in data science, machine learning, or related fields.
• Proficiency in Python and ML frameworks (scikit-learn, XGBoost, Light
  
  GBM, 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.

• 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

• 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.

When you join Salesforce, you’ll be limitless in all areas of your life. Our benefits and resources support you to find balance and be your best, and our AI agents accelerate your impact so you can do your best. Together, we’ll bring the…