Description
Salesforce is seeking a Principle Researcher, Workforce Intelligence with a specialized focus on Machine Learning. In this role, you will partner with executive leadership and C-suite stakeholders to set strategic research priorities, leveraging deep technical expertise to build ML models that provide accurate predictions and actionable insights for our most complex talent and organizational questions.
You will drive end-to-end impact - from problem formulation through model development to enabling data-driven leadership decisions. You will also champion a long-term, future-focused vision that integrates advanced AI and Large Language Models (LLMs) into the fabric of our workforce strategy. By balancing technical rigor with business agility, you will ensure our models drive tangible, positive organizational growth.
Key Responsibilities
Strategic ML Leadership: Drive the long-term vision for predictive and prescriptive workforce intelligence by authoring new ML strategies that support Employee Success partners and enhance employee experience initiatives.
Advanced Research Design: Define complex business challenges as rigorous research questions and develop novel methodological approaches when standard techniques are insufficient.
Predictive & Causal Modeling: Lead the development of robust, well-calibrated machine learning models and causal identification strategies to generate forward-looking insights that help us understand the drivers of organizational success.
Evidence-Based Impact: Lead impact attribution studies for workforce programs, utilizing advanced statistical methods to determine the effectiveness of talent initiatives and guide future investments.
Multimodal Data Intelligence: Lead the extraction of insights from unstructured enterprise data to provide a holistic, 360-degree understanding of organizational sentiment and the employee experience.
Scalable Insight Tools: Drive the evolution of research into scalable, automated tools and workflows that provide leaders with real-time, data-driven guidance for strategic workforce planning.
Tailored Workforce Insights: Utilize advanced modeling to identify how different talent initiatives impact various employee segments, ensuring workforce strategies are inclusive and optimized for a diverse global population.
Executive Stakeholder Management: Partner with and advise senior leadership (SVP+ and C-suite), providing strategic counsel and influencing organizational strategy at the highest levels.
Technical Infrastructure: Oversee the development of robust data infrastructure and MLOps (e.g., Airflow, AWS/GCP) to ensure the secure and reliable deployment of models at scale.
Innovation & Technical Evangelism: Remain at the forefront of emerging AI and LLM trends, ensuring they integrate seamlessly into a human-centric talent model that prioritizes the employee experience.
Preferred Qualifications
Education: Master’s or PhD in a highly quantitative field (e.g., Computer Science, Data Science, Mathematics, I/O Psychology, Econometrics, or Statistics).
Experience: 10+ years in data science or applied research, with a proven track record of leading large-scale AI/ML projects and delivering scientific insights to executive-level leadership.
Technical Acumen: Expert-level proficiency in Python, R, and SQL. Deep experience with ML frameworks (e.g., PyTorch, TensorFlow, Scikit-learn) and advanced techniques including RAG-based solutions and LLMs.
Engineering Mindset: Experience in architecting secure data enclaves and implementing MLOps to harden production data pipelines.
Communication: Exceptional skills in translating complex technical concepts for non-technical audiences, supported by a history of peer-reviewed publications or high-impact technical briefings.
Strategic Influence: Experience working with executive stakeholders to influence high-level workforce planning and organizational strategy using data-driven insights.
Autonomy: Proven ability to operate with a high degree of autonomy, making strategic decisions in a fast-paced, complex environment.