Lead, People AI

Company: Piramal Finance

Experience: 7 - 11 years

Location: Bengaluru

About the Role:

We are looking for a Lead, People AI to drive data-led decision-making across workforce management, employee performance, productivity, and organisational effectiveness. The ideal candidate will combine strong Data Science, AI/ML, and People Analytics expertise with proven people management and leadership skills.

The role will focus on building AI-driven solutions and analytical models across employee attrition, incentive effectiveness, Performance Management Systems (PMS), and workforce productivity. The candidate will lead and mentor a team of data scientists and analysts, collaborate with senior stakeholders, and translate complex workforce data into actionable business insights.

Key Responsibilities:

1. People Analytics and Workforce Intelligence:

- Lead People Analytics, Workforce Analytics, and People AI initiatives to support strategic workforce planning and organisational decision-making.

- Analyse employee lifecycle data to identify trends and opportunities to improve workforce effectiveness, productivity, and retention.

- Develop analytical frameworks to measure employee performance, engagement, productivity, and organisational health.

- Translate complex workforce data into actionable insights and recommendations for senior leadership.

2. Attrition and Retention Analytics:

- Develop statistical and predictive models to identify attrition drivers, retention risks, and employee turnover patterns.

- Analyse factors influencing attrition, including compensation, performance, tenure, career progression, and organisational dynamics.

- Build data-driven interventions to improve employee retention and support proactive workforce planning.

- Measure the effectiveness of retention strategies through analytics and outcome tracking.

3. Incentive and Performance Analytics:

- Analyse employee incentive structures, variable pay, and reward programmes to evaluate their effectiveness and business impact.

- Develop analytical models to assess the relationship between incentives, employee performance, productivity, and business outcomes.

- Drive data-led insights for Performance Management Systems (PMS), including performance distribution, rating trends, goal achievement, and performance differentiation.

- Identify opportunities to improve incentive effectiveness, performance measurement, and reward frameworks.

4. AI/ML and Advanced Analytics:

- Apply statistical modelling, machine learning, and AI techniques to solve complex people and workforce-related problems.

- Develop predictive and prescriptive analytics solutions for attrition, productivity, employee performance, and workforce planning.

- Identify opportunities to leverage AI and automation to improve HR processes and decision-making.

- Build scalable analytical solutions, dashboards, and decision-support tools that enable business teams to act on insights.

5. People Management and Team Leadership:

- Lead, manage, and mentor a team of data scientists and analysts, providing direction, coaching, and regular feedback.

- Set clear goals, define priorities, and ensure timely delivery of high-quality analytical projects.

- Build a collaborative, high-performance team culture that encourages ownership, innovation, continuous learning, and accountability.

- Support team members in developing their technical, analytical, and problem-solving capabilities.

- Review analytical approaches, model outputs, and project deliverables to ensure quality, accuracy, and business relevance.

- Manage resource allocation, project planning, and competing priorities while ensuring alignment with business objectives.

- Identify opportunities to strengthen team capabilities in AI, machine learning, advanced analytics, and People Analytics.

6. Stakeholder Management and Business Partnership:

- Partner with HR, business leaders, Finance, and technology teams to identify business challenges and translate them into analytical solutions.

- Present insights, recommendations, and business impact to senior stakeholders in a clear and actionable manner.

- Influence business decisions through data-backed recommendations and measurable outcomes.

- Establish best practices for data quality, model validation, analytics governance, and the responsible use of employee data.

What We're Looking For:

- 7 - 11 years of relevant experience in Data Science, People AI, People Analytics, Workforce Analytics, or HR Analytics.

- Strong hands-on experience in one or more of the following areas: employee attrition, incentive analytics, PMS/performance analytics, workforce productivity, or people analytics.

- Proven people management experience, including leading teams, mentoring professionals, setting goals, and managing performance and delivery.

- Experience applying statistical modelling, machine learning, and predictive analytics to real-world business problems.

- Strong proficiency in Python and SQL, with experience using data visualisation tools such as Power BI or Tableau.

- Ability to work with large and complex datasets, identify meaningful patterns, and translate findings into business recommendations.

- Strong stakeholder management, communication, decision-making, and problem-solving skills.

- Ability to balance technical depth with team leadership and business priorities.

Good to Have:

- Experience building AI-driven solutions for HR or workforce management.

- Exposure to compensation analytics, incentive design, performance management, employee engagement, and workforce planning.

- Experience working with HRIS platforms and integrating data from multiple enterprise systems.

- Experience in BFSI, fintech, or other large, data-driven organisations.

What Success Looks Like:

- Improved understanding and prediction of employee attrition.

- Better visibility into workforce productivity and performance drivers.

- Data-backed improvements in incentive effectiveness and PMS outcomes.

- Successful adoption of AI-driven solutions across people and workforce-related decisions.

- A high-performing analytics team with strong technical capabilities, ownership, and accountability.

- Measurable business impact through effective team leadership, stakeholder collaboration, and data-driven decision-making.