Job Overview
Role: Intern Data Science Location: Hyderabad Experience: Relevant experience or exposure in Data Science Qualification: Graduate or postgraduate qualification in a quantitative discipline Key Skills: Statistical Modeling, Machine Learning, Regression, Classification, Clustering, Design of Experiments, Statistical Inference, Data Mining, EDA, Feature Engineering, Predictive Modeling, Data Visualization, LLMs, Generative AI
Job Description
Novartis Internship 2026 is an early-career opportunity for candidates interested in Data Science, Machine Learning, Predictive Analytics, Generative AI and workforce analytics. The Data Science Intern position is based in Hyderabad, India and is part of the People & Organization division under the Human Resources business unit. The internship focuses on applying data science to real-world workforce challenges such as employee engagement, recruitment optimization, predictive workforce planning and Generative AI use cases. Interns will work on statistical models, machine learning, dashboards, exploratory data analysis and LLM-based solutions with visibility to stakeholders and business leaders.
Roles and Responsibilities
- Data Science Application: Apply data science techniques to employee engagement challenges; Support recruitment optimization and predictive workforce planning initiatives.
- Model & Dashboard Development: Develop models, dashboards and analytical frameworks that support enterprise-level decisions; Build self-service dashboards and visualizations for stakeholders.
- Research & Analysis: Support research and development of new algorithms, statistical models, methods and business models; Provide insights from structured and unstructured data; Conduct statistical and machine learning analysis for business problems; Perform data mining, exploratory data analysis and feature engineering.
- Predictive Modeling: Develop predictive models for talent acquisition, employee experience, location strategy and workforce planning.
- Generative AI: Use Generative AI to support personalized employee experiences and HR workflows; Explore and prototype LLM-based workforce analytics use cases.
- Collaboration: Collaborate on high-impact projects with stakeholders and leaders.
Skills and Eligibility Criteria
Educational Background: Graduate or postgraduate qualification in a quantitative discipline. Graduation or post-graduation from a top-tier university is preferred.
Experience: Relevant experience or exposure in Data Science.
Mandatory Technical Skills:
- Regression (GLM, non-linear)
- Classification (Decision trees, Random Forest, Boosting, SVM, CART)
- Clustering
- Design of Experiments
- 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:
- Strong understanding of statistical modeling and machine learning techniques
- Ability to apply quantitative methods to business and workforce-related problems
- Strong analytical ability