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Role: Data Scientist

Must Have Skills:

· Bachelor’s degree in Data Science, Statistics, Computer Science, Economics, Engineering, or related field; advanced degree preferred.

· 7+ years of applied data science experience, with at least 5 years in Talent/People Analytics, or consulting for large enterprises.

· Demonstrated experience delivering end-to-end analytics and deploying models to production in cross-functional environments.

· Strong experience with HR systems and data models (Workday, PeopleSoft) or equivalent enterprise HR data experience.

· Modeling & methods: strong foundations in statistical modeling (linear/logistic regression, survival analysis/time-to-event where relevant), tree-based methods, clustering, causal methods, and applied NLP/transformer/LLM techniques for text- based HR applications.

· Programming: production-capable Python coding (modular design, testing, packaging) experience with version control (Git), and collaboration with DevOps/CI-CD workflows.

· Data engineering & infrastructure: experience working with ETL, feature engineering, data warehouses/lakes, and modern cloud platforms; familiarity with Spark, dbt, Airflow, or equivalents desirable.

· Model lifecycle & tooling: familiarity with model registries and lifecycle tools (MLflow, Seldon, Terraform/Helm or equivalent), explainability tools (SHAP, LIME), fairness/tooling (AIF360 or equivalent), and monitoring frameworks.

· Querying & visualization: advanced SQL skills; experience with BI/visualization tools (Tableau, Power BI) and producing executive-ready dashboards and narratives.

· Privacy & security: practical knowledge of de-identification, synthetic data, and access-control patterns for sensitive HR data.

Roles & Responsibilities

· 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 remediation 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.

 Managerial Skills:

· 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