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About Salesforce

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.

Ready to level-up your career at the company leading workforce transformation in the agentic era? You’re in the right place! Agentforce is the future of AI, and you are the future of Salesforce.

Salesforce is seeking a Principle Researcher, Workforce Intelligence with a specialized focus on Machine Learning. In this role, you will work alongside ES executive leadership, Employee Success Business Partners, and C-suite stakeholders to set strategic research priorities, leveraging your deep technical expertise to build ML models that provide accurate predictions and actionable explanations for our most complex business questions.

You will drive end-to-end impact from problem formulation through model development to influencing and enabling business decisions and outcomes. You will also drive the long-term, future-focused vision for the organization, authoring and championing new strategies that integrate advanced AI, Agentic workflows, and Large Language Models (LLMs) into the fabric of our workforce decision-making. You will balance technical rigor with business speed, ensuring that our models don't just achieve high accuracy but also drive tangible organizational change.

Key Responsibilities

• Strategic ML Leadership: Drive the long-term vision for predictive, prescriptive, and causal workforce intelligence by authoring and championing new ML strategies across multiple departments, including Employee Success Business Partners, Employee Listening and productizing data initiatives. Define ambiguous business problems as rigorous research questions and develop novel methodological approaches when standard techniques are insufficient. Drive scientific rigor in modeling, validation, and interpretation, setting the bar for research quality across the organization.

• Predictive & Prescriptive Modeling: Lead the development of robust, well-calibrated machine learning models on structured workforce data (e.g., predictive modeling, forecasting, and segmentation) to capture complex patterns and generate forward-looking insights. Operationalize model outputs into prescriptive recommendations, optimization strategies, and decision frameworks that drive measurable business impact at scale.

• Causal Inference & Experimentation: Drive rigorous evaluation of HR programs and workforce interventions using advanced statistical and econometric methods (e.g., A/B testing, quasi-experimental design). Establish clear treatment/control frameworks and causal identification strategies to determine what truly works. Identify heterogeneous treatment effects to inform targeted and personalized workforce interventions. Translate causal findings into concrete policy and program recommendations, including expected impact and trade-offs.

• Unstructured Data Intelligence (NLP & LLMs): Lead the extraction of insights from unstructured employee data (e.g., surveys, feedback, V2MOMs) using advanced NLP and large language models. Develop scalable approaches for text representation, classification, and theme discovery (e.g., topic modeling, embedding-based clustering, and LLM-driven labeling) to surface emerging patterns, sentiment, and risk signals. Integrate unstructured signals with structured data to enrich downstream modeling and business insights. Establish evaluation and validation frameworks to ensure reliability, consistency, cost-efficiency, and scalability of LLM-driven insights in production environments.

• Executive Stakeholder Management: Partner with and advise senior leadership (SVP+ and C-suite), providing strategic counsel and influencing at the highest levels of the organization.

• Prioritization & Impact: Evaluate and prioritize competing, high-priority ML projects across the organization, making difficult decisions to balance rigor with the need for rapid business impact. Define success metrics, evaluation frameworks, and measurement strategies for workforce initiatives, ensuring alignment between model outputs and business KPIs.

• Data Storytelling & Influencing: Lead the strategic narrative on workforce data, translating complex ML outputs into clear, compelling narratives that drive leadership decisions and organizational change.

• Innovation & Technical Evangelism: Remain at the forefront of emerging AI, Agents, and LLMs; champion their adoption and ensure they integrate seamlessly with existing business tools and "agentic" talent models.

• Scalable Architecture: Oversee the development of robust data infrastructure and MLOps to ensure the reliable deployment of models at scale, including feature engineering frameworks, data modeling, and data quality standards.

• Productizing Data: Drive the 'productization' of data by transitioning ad-hoc research into scalable, automated tools and agents that provide real-time insights to the business.

• Team Development & Culture: Lead and mentor project teams of data scientists and researchers, fostering a culture of high performance and continuous learning while developing the technical capabilities of the team.

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 of experience 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. Experience working with large-scale noisy, and heterogeneous enterprise data.

• Advanced 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 predictive and causal machine learning, RAG-based solutions, and LLMs.

• Product & Engineering Mindset: Experience in productizing data, architecting secure data enclaves, and implementing MLOps (Airflow, AWS/GCP) to harden production data pipelines.

• Communication: Exceptional written and verbal communication skills, including a track record of translating complex technical concepts for non-technical C-suite audiences and a history of peer-reviewed publications or high-impact technical briefings. Experience working with executive stakeholders to influence policy, compensation, or organizational design decisions using data

• Autonomy: Proven ability to operate with a high degree of autonomy, setting direction and making strategic decisions with minimal oversight in a fast-paced, complex environment.

Unleash Your Potential

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 power of Agentforce to organizations of all sizes and deliver amazing experiences that customers love. Apply today to not only shape the future — but to redefine what’s possible — for yourself, for AI, and the world.

Accommodations

If you need a reasonable accommodation during the application or the recruiting process, please submit a request via this Accommodations Request Form.

Please note that Salesforce uses artificial intelligence (AI) tools to help our recruiters assess and evaluate candidates’ resumes and qualifications throughout the recruiting process. Humans will always make any candidate selection and hiring decisions. Please see our Candidate Privacy Statement for more information about how we use your personal data and your rights, including with regard to use of AI tools and opt out options.

Posting Statement

Salesforce is an equal opportunity employer and maintains a policy of non-discrimination with all employees and applicants for employment. What does that mean exactly? It means that at Salesforce, we believe in equality for all. And we believe we can lead the path to equality in part by creating a workplace that’s inclusive, and free from discrimination. Know your rights: workplace discrimination is illegal. Any employee or potential employee will be assessed on the basis of merit, competence and qualifications – without regard to race, religion, color, national origin, sex, sexual orientation, gender expression or identity, transgender status, age, disability, veteran or marital status, political viewpoint, or other classifications protected by law. This policy applies to current and prospective employees, no matter where they are in their Salesforce employment journey. It also applies to recruiting, hiring, job assignment, compensation, promotion, benefits, training, assessment of job performance, discipline, termination, and everything in between. Recruiting, hiring, and promotion decisions at Salesforce are fair and based on merit. The same goes for compensation, benefits, promotions, transfers, reduction in workforce, recall, training, and education.

In the United States, compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for incentive compensation, equity, and benefits. Salesforce offers a variety of benefits to help you live well including: time off programs, medical, dental, vision, mental health support, paid parental leave, life and disability insurance, 401(k), and an employee stock purchasing program. More details about company benefits can be found at the following link: https://www.salesforcebenefits.com.

At Salesforce, we believe in equitable compensation practices that reflect the dynamic nature of labor markets across various regions. The typical base salary range for this position is $164,000 - $261,500 annually. The range represents base salary only, and does not include company bonus, incentive for sales roles, equity or benefits, as applicable.