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Intern Data Science

Novartis
Hyderabad, IND
Internship, Full Time
Entry
Onsite
Discovered Yesterday
Machine LearningPredictive AnalyticsLarge Language Models (LLMs)Generative AIStatistical ModelingRegression
Free

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Key skills for this role

Machine LearningPredictive AnalyticsLarge Language Models (LLMs)
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Responsibilities

  • 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

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