Intern AI & Data Science
Novartis has closed or removed this posting -- see current similar openings below.
About this opportunity
The AI & Data Science Operations team works on building data-driven and AI-powered solutions to improve business decision-making at scale.As an MTech Intern, you will work closely with data engineers and domain SMEs on real-world data, pipelines, and AI prototypes, gaining hands-on exposure to data engineering, machine learning, and emerging GenAI use cases.This internship is designed to provide strong industry grounding, not just academic experimentation.
Key ResponsibilitiesAssist in data preparation, transformation, and analysis using SQL and Python/PySparkWrite and optimize SQL queries to extract and analyze data from structured datasetsSupport development of data pipelines and feature engineering workflowsAssist in building and testing Agentic AI modules under guidance (including GenAI use cases)Work with senior team members to prototype Agentic AI or GenAI-driven solutionsPerform data quality checks and basic validation of datasetsDocument learnings, code, and analysis in a clear and structured mannerCollaborate with team members following Agile / sprint-based execution Required QualificationsCurrently pursuing MTech / MS in:Computer Science with Data Science/AI as subjectsOr a closely related field Core Technical SkillsSQL – writing queries, joins, aggregationsPython – data processing, basic scriptingExposure to PySpark or Spark SQL (coursework or projects is sufficient)Basic understanding of:Data structuresDatabasesData processing conceptsFundamentals of Machine Learning (regression, classification, basic evaluation metrics)Awareness of Large Language Models (LLMs), Generative AI concepts, Agentic AIAny academic or personal project involving:ML modelsNLPChatbotsAI prototypesHands-on depth is not expected, curiosity and willingness to learn is.PreferredExposure to cloud platforms (AWS / Azure / GCP) through coursework or labsExperience with Git / version controlExperience working on college projects, hackathons, or research workStrong analytical thinking and problem-solving mindsetSoft Skills & MindsetStrong learning orientation and curiosityAbility to break down problems and ask the right questionsClear communication of ideas and findingsComfortable working in a team-based environment What the Intern Will GainHands-on exposure to real enterprise data and use casesPractical experience in SQL, Python, PySpark, and data pipelinesIntroduction to AI/ML and GenAI applications in industryMentorship from experienced Data Scientists and Engineers
Job details
How this role compares
Computed from every other active Biostatistics & Data Science role in our database, not just this employer's listings.
We currently track 52 comparable Intern/Fellow/Postdoc Biostatistics & Data Science roles across 16 biopharma companies.
Salary context
15 of 52 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 52 comparable roles
+ 2 more countries
Seniority mix
52 of 52 peers have a known seniority level
Therapeutic area mix
4 of 52 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.