Job Overview
Role: Intern Data Science Location: Hyderabad Experience: Early Career (Fixed Term) Qualification: Graduate or postgraduate qualification in a quantitative discipline Key Skills: Statistical Modeling, Machine Learning, Regression, Classification, Clustering, Design of Experiments, Statistical Inference, Predictive Models, Data Mining, Exploratory Data Analysis, Feature Engineering, Data Visualization, Generative AI, LLMs
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 and support recruitment optimization and predictive workforce planning initiatives.
- Model Development: Develop models, dashboards, and analytical frameworks that support enterprise-level decisions.
- 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 Analysis: Provide insights from structured and unstructured data, conduct statistical and machine learning analysis for business problems, and perform data mining, exploratory data analysis, and feature engineering.
- Predictive Modeling: Develop predictive models for talent acquisition, employee experience, location strategy, and workforce planning.
- Dashboarding: Build self-service dashboards and visualizations for stakeholders.
- Generative AI: Use Generative AI to support personalized employee experiences and HR workflows, and explore and prototype LLM-based workforce analytics use cases.
Skills and Eligibility Criteria
Educational Background: Graduate or postgraduate qualification in a quantitative discipline. Graduation or post-graduation from a top-tier university is mentioned as preferred.
Experience: Relevant experience or exposure in Data Science. Designed for early-career candidates.
Mandatory Technical Skills:
- Statistical Modeling (Regression, Generalized Linear Models, non-linear regression)
- Classification (Decision trees, Random Forest, Boosting, SVM, CART)
- Clustering (Unsupervised learning, 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:
- Strong analytical ability
- Ability to apply quantitative methods to business and workforce-related problems
- Interest in healthcare technology and business applications