Job Overview
Role: Data Science Intern, People & Organization Division, Human Resources Business Unit Location: Hyderabad Experience: Intern (Early Career) Qualification: Graduate or postgraduate qualification in a quantitative discipline Key Skills: Statistical Modeling, Machine Learning (Regression, Classification, Clustering, Design of Experiments), Statistical Inference, Predictive Modeling, Exploratory Data Analysis, Feature Engineering, Data Visualization, Generative AI, LLMs, Workforce Analytics
Job Description
Novartis is offering a Data Science Intern position in Hyderabad for 2026, within the People & Organization division under Human Resources. This early-career opportunity focuses on applying data science to real-world workforce challenges like employee engagement, recruitment optimization, predictive workforce planning, and Generative AI use cases. Interns will work on statistical models, machine learning, dashboards, and LLM-based solutions, with high visibility to stakeholders. Candidates need a graduate or postgraduate qualification in a quantitative discipline, ideally from a top-tier university, with relevant data science experience. Strong understanding of statistical modeling and ML techniques (Regression, Classification, Clustering, Design of Experiments) is essential. Generative AI is a key component, exploring applications for personalized employee experiences and HR workflows. The role also requires strong analytical ability for data mining, EDA, feature engineering, and predictive modeling.
Roles and Responsibilities
- Data Science Application: Apply data science techniques to employee engagement challenges.
- Workforce Optimization: Support recruitment optimization and predictive workforce planning initiatives.
- Model and Dashboard Development: Develop models, dashboards, and analytical frameworks that support enterprise-level decisions.
- Stakeholder Collaboration: Collaborate on high-impact projects with stakeholders and leaders.
- Research and Development: Support research and development of new algorithms, statistical models, methods, and business models.
- Data Insights: Provide insights from structured and unstructured data.
- Statistical and ML Analysis: Conduct statistical and machine learning analysis for business problems.
- Predictive Model Development: Develop predictive models for talent acquisition, employee experience, location strategy, and workforce planning.
- Self-Service Tools: Build self-service dashboards and visualizations for stakeholders.
- Data Analysis: Perform data mining, exploratory data analysis, and feature engineering.
- Generative AI Application: Use Generative AI to support personalized employee experiences and HR workflows.
- LLM Prototyping: Explore and prototype LLM-based workforce analytics use cases.
Skills and Eligibility Criteria
Educational Background: Graduate or postgraduate qualification in a quantitative discipline (top-tier university preferred).
Experience: Relevant experience or exposure in Data Science (Early Career).
Mandatory Technical Skills:
- Statistical Modeling (Regression, Generalized Linear Models (GLM), non-linear regression)
- Classification (Decision trees, Random Forest, Boosting, SVM, CART)
- Clustering (Unsupervised learning and segmentation techniques)
- Design of Experiments (Experimental design and analysis)
- Statistical Inference
- Machine Learning (building and evaluating predictive models)
- Data Mining
- Exploratory Data Analysis (EDA)
- Feature Engineering
- Predictive Modeling
- Data Visualization
- Dashboard Development
- Structured and Unstructured Data Analysis
Competencies:
- Generative AI (for employee experiences, AI-assisted HR workflows, decision-support applications, LLM-based workforce analytics, prototyping AI use cases)
- Strong Analytical Ability