Manager Data Science HR

We are looking for a Manager – People Analytics to drive data-driven decision-making in HR by leveraging advanced analytics techniques such as regression models, simulations, and machine learning to generate actionable insights.

The role involves partnering closely with global HR teams—including Talent Acquisition, Talent Development, and Organizational Effectiveness—to design scalable analytical solutions across diverse use cases.

You will prepare and transform high-quality datasets for meaningful visualization, and effectively communicate insights to stakeholders to support strategic decisions.

Additionally, you will contribute to building a data-first HR culture by promoting innovative analytics practices, defining data requirements, documenting methodologies, and enabling knowledge sharing to upskill the organization while strengthening the People Analytics community.

• Evaluate, implement, and apply analytical methods, tools, and processes (e.g., regression models/GLM, simulations, machine learning)to generate actionable diagnostic, predictive, and prescriptive insights.
• Collaborate with HR Global Functional Teams (GFT) (e.g., Talent Acquisition, Talent Development, Organizational Effectiveness) to develop scalable analytical approaches across a variety of use cases.
• Identify and prepare relevant data sets to explore opportunities to enhance data quality and collection, and prepare data (e.g., using Tableau Prep Builder) for visualization.
• Interpret and communicate insights clearly to stakeholders, providing data-driven decision support to HR business partners and leadership.
• Contribute to making HR a data-driven organization by identifying innovative data science practices and defining data requirements.
• Document methodologies and best practices, and support the up skilling of key stakeholders through knowledge sharing.
• Actively contribute to the growth of our People Analytics Community.

Your Profile Qualifications and skills to help you succeed

• A degree focused on empirical or statistical methods, such as Social Sciences, Psychology, Business Administration, Economics, Computer Science, Machine Learning, Mathematics, or Statistics; ideally a Master’s degree.