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, Predictive Analytics, Generative AI, Workforce Analytics, Regression, Classification, Clustering, Design of Experiments, Statistical Inference
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.
- HR Initiative Support: Support recruitment optimization and predictive workforce planning initiatives.
- Model Development: Develop models, dashboards and analytical frameworks that support enterprise-level decisions.
- Project Collaboration: Collaborate on high-impact projects with stakeholders and leaders.
- Research & Development: Support research and development of new algorithms, statistical models, methods and business models.
- Insight Generation: Provide insights from structured and unstructured data.
- Statistical 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.
- Dashboard Development: Build self-service dashboards and visualizations for stakeholders.
- Data Analysis: Perform data mining, exploratory data analysis and feature engineering.
- Generative AI Use: 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. Graduation or post-graduation from a top-tier university is mentioned as preferred.
Experience: Relevant experience or exposure in Data Science
Mandatory Technical Skills:
- 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 (Applying statistical methods to draw conclusions from data)
- 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
- Strong analytical ability
- Practical knowledge of LLMs, prompt-based applications, AI APIs or Generative AI projects (for Generative AI and LLM Opportunities)