HR -Data Scientist

Lead end-to-end analytic projects: define problem statements with HR stakeholders, design experiments, select appropriate methods, develop models, validate results, and deliver production-ready solutions and monitoring.

Build predictive and prescriptive models for talent use cases (attrition/retention, internal mobility, promotion forecasting, performance indicators, recruitment sourcing/scoring, skilling/curation, compensation analytics).

Develop and productionize features and models in collaboration with data engineers and ML engineers: implement reproducible ETL, feature pipelines, model training pipelines, CI/CD, and deployment patterns.

Apply statistical methods, hypothesis testing, causal inference where appropriate, and robust validation (cross-validation, holdouts, calibration, fairness testing) to ensure reliable, defensible results.

Design and operationalize NLP/LLM solutions for HR use cases (resume parsing, candidate experience, employee feedback analysis) while enforcing privacy, data minimization and explainability requirements.

Instrument model monitoring and drift detection; define alerting, retraining triggers, and remediati on plans.

Produce clear, actionable visualizations and dashboards that tell the story of analytic findings and drive decisions; collaborate with BI developers to operationalize reporting.

Translate technical analyses into business recommendations, quantify expected impact, and work with partners to implement changes and measure outcomes.

Mentor junior data scientists/analysts, review code and model artifacts, and help raise team standards for reproducibility, documentation, and governance.

Ensure models and data products adhere to governance, privacy, and ethical requirements; collaborate with HR Data Steward, Legal/Privacy, and Ethics/AI governance on reviews and approvals. Generic Managerial Skills, If any

Problem-solver with product mindset: frames analytics as business products with clear KPIs and adoption plans.

Ownership & results orientation: takes accountability for delivery, end-to-end operation, and measurable impact.

Communication & storytelling: synthesizes complex analyses into concise recommendations for HR leaders and executives.

Collaboration & influence: builds strong cross-functional relationships and navigates competing priorities.

Coaching & development: mentors peers and contributes to team capability growth.

Ethical judgment: prioritizes fairness, privacy, and employee impact in modelling decisions.