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IDEMIA

HR Data Scientist

IDEMIA Noida, Uttar Pradesh, India

Software Development · 10,001+ employees

Oct 22
data-scientist Senior (5-10 yrs) Full-time India
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About the role

This role involves developing predictive models and workforce analytics to drive data-informed decisions regarding talent acquisition, retention, and performance. The incumbent will also manage data quality, ensure compliance with privacy regulations, and build dashboards to support HR leadership.

What they look for

Predictive Modeling Workforce Analytics Data Visualization Power BI Data Governance Data Quality SuccessFactors Strategic Workforce Planning Talent Acquisition Analytics Machine Learning Automation Data Integration GDPR Compliance Responsible AI Statistical Analysis

Requirements

The position requires expertise in HR data systems, predictive modeling, and data governance to translate complex business questions into actionable insights. Candidates must be proficient in data integration and communicating technical findings to non-technical stakeholders.

Full description

Purpose

This role partners with HRIS and HR teams to transform workforce data into predictive models, analytics, and actionable insights, enabling data-driven decisions on talent acquisition, retention, performance, and workforce planning, while ensuring data quality, privacy, and compliance across HR systems.

Key Missions

Predictive modeling: Design and deploy models to forecast attrition, internal mobility, hiring needs, and workforce trends.

Workforce analytics: Analyze HR data (headcount, turnover, engagement, performance, compensation) to identify patterns and deliver actionable insights.

Workforce planning: Support strategic workforce planning with scenario modeling and skills gap analysis.

Talent acquisition analytics: Measure and optimize recruitment funnels, time-to-hire, sourcing effectiveness, and quality of hire.

Dashboards and reporting: Build and maintain self-service dashboards and KPIs (e.g., Power BI) for HR leadership and business stakeholders.

Data quality and governance: Define and monitor data quality standards across HR systems, and work with HRIS teams to fix root causes of data issues.

Data integration: Collaborate with HRIS and IT teams to consolidate data from SuccessFactors and other HR platforms into a reliable analytics environment.

Privacy and ethics: Ensure that models and analyses comply with GDPR and internal policies, and apply responsible AI principles (bias detection, fairness, explainability).

Stakeholder partnership: Translate business questions from HR and business leaders into analytical approaches, and communicate findings in clear, non-technical language.

Innovation and continuous improvement: Identify opportunities for AI/ML and automation in HR processes, and promote a data-driven culture within the HR function.

Profile & Other Information

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