HR -Data Scientist

Must Have Technical/Functional 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 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.

Base Salary Range : $110,000 to $140,000 Per Annum

TCS Employee Benefits Summary

Discretionary Annual Incentive.

Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.

Family Support: Maternal & Parental Leaves.

Insurance Options: Auto & Home Insurance, Identity Theft Protection.

Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.

Time Off: Vacation, Time Off, Sick Leave & Holidays.

Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

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Skills

Access Control, Analysis Skills, Artificial Intelligence (AI), Business Analysis, Business Intelligence, Calibration, Candidate Sourcing, Cloud Computing, Coaching, Code Reviews, Compensation Analysis, Computer Science, Consulting, Continuous Deployment/Delivery, Continuous Integration, Cross-Functional, Data Analysis, Data Modeling, Data Science, Data Warehousing, Database Extract Transform and Load (ETL), DevOps, Documentation Standards, Economics, Establish Priorities, Experiment Design, Forecasting, Git, Human Resources Software, Legal, Linear Models, Loan Funding, Machine Tool, Mentoring, Model Review, Model Validation, Natural Language Processing (NLP), Operational Measurement, Performance Metrics, Power BI, Predictive Modeling, Privacy Controls, Problem Solving Skills, Python Programming/Scripting Language, Reporting Dashboards, SQL (Structured Query Language), Software Engineering, Source Code/Configuration Management (SCM), Statistical Modeling, Statistics, Storytelling, Student Loans, Tableau, Technical Analysis, Test Design, Testing, Use Cases

About the Company

Tata Consultancy Services Ltd