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Intern Data Science

Overview
Novartisnovartis.comPosted 7 weeks ago
Hyderābād, Telangana, INOn-site
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Seniority level
Entry
Employment type
Intern
Specialization
Data science

Key skills

  • Data Science
  • Generative AI
  • Machine Learning
  • LLM
  • Recruiting
  • Data Analysis

Job Description Summary

As a data science intern, you will:

  • Apply your skills to solve real-world challenges related to employee engagement, recruitment optimization, predictive workforce planning, and generative AI use cases.
  • Design models, dashboards, and frameworks that influence enterprise-wide decisions.
  • Collaborate on high-impact projects with direct visibility to stakeholders and leaders.
  • Gain hands-on experience with cutting-edge data science methodologies and tools, including machine learning, predictive analytics, large language models (LLMs), and generative AI.
  • Build tangible solutions that shape the future of the workforce—strategy, processes, and culture.
  • Support supervisor in research and development of new algorithms, methods, statistical models and business models and providing insight into structured and unstructured data.

Your impact will include

  • Leverage statistical and machine learning techniques such as regression, classification, clustering, and design of experiments.
  • Develop predictive models to address key HR areas like talent acquisition, employee experience, location strategy, and workforce planning.
  • Build self-service dashboards and visualizations that empower stakeholders with real-time insights.
  • Conduct data mining, exploratory data analysis (EDA), and feature engineering to uncover actionable insights.
  • Use generative AI to create personalized employee experiences, optimize HR workflows, and support decision-making.
  • Explore and prototype LLM-based use cases to revolutionize workforce analytics.

What you’ll bring to the role

  • Relevant experience in Data Science
  • Post-graduate or Graduate from a top Tier University in a quantitative discipline
  • Technical Skills: Statistical Modeling/Machine Learning. Must have: Statistical and Machine Learning techniques like Regression (esp., GLM, non-linear, etc.), Classification (Trees, Random Forest, Boosting, SVM, CART, etc.) Clustering, Design of Experiments, Statistical Inference