Data Science - Internship

About the internship:Selected intern's day-to-day responsibilities include:

We are looking for a hands-on Data Scientist Intern who is interested in solving real business problems using data, machine learning, and AI.

The intern will work on live datasets across domains such as manufacturing, healthcare/pharma, financial analytics, and enterprise decision intelligence. The role will involve exploratory data analysis, feature engineering, predictive modelling, model evaluation, and increasingly, the use of LLMs and GenAI as part of modern data science workflows.

We are looking for someone who is strong in fundamentals, comfortable working independently with data, and willing to experiment, learn, and build.

Key Responsibilities

Clean, preprocess, validate, and analyze structured and semi-structured datasets.

Perform detailed exploratory data analysis (EDA) and identify patterns, relationships, anomalies, and data-quality issues.

Develop and evaluate machine learning models for classification, regression, forecasting, clustering, and other business problems.

Perform feature engineering and identify relevant variables for modelling.

Establish baselines and evaluate models using appropriate statistical and ML metrics.

Use Python and/or R for data analysis, visualization, statistical analysis, and modelling.

Work with SQL and databases to extract and analyze data.

Build clear visualizations and analytical summaries for technical and business users.

Experiment with LLMs/GenAI for activities such as data understanding, automated analysis, insight generation, and model-assisted workflows.

Support development of reusable analytical pipelines and AI/ML components.

Work closely with senior data scientists, developers, and business teams on client and product initiatives.

Clearly document datasets, assumptions, methodology, experiments, models, and findings.

Present findings in a simple and business-understandable manner.

Required Skills

Good working knowledge of Python.

Familiarity with libraries such as:

Pandas

NumPy

Scikit-learn

Matplotlib / Seaborn

Good understanding of:

Statistics and probability

Exploratory data analysis

Regression and classification

Feature engineering

Model evaluation

Basic knowledge of SQL and relational databases.

Ability to understand unfamiliar datasets and independently investigate them.

Strong analytical and problem-solving skills.

Ability to clearly explain analytical findings.

Good to Have

Experience with R and statistical modelling.

Exposure to time-series analysis and forecasting.

Familiarity with XGBoost, LightGBM, CatBoost or similar ML libraries.

Exposure to Generative AI, LLMs, prompt engineering, RAG, or AI agents.

Familiarity with open-source LLMs such as Qwen, Llama, or similar models.

Experience using APIs and integrating AI/ML models into applications.

Familiarity with Git/GitHub or GitLab.

Exposure to cloud environments, Jupyter/JupyterHub, Docker, or similar development environments.

Educational Qualification

Bachelor's or Master's degree in:

Computer Science

Data Science / Artificial Intelligence

Statistics

Mathematics

Engineering

Econometrics / Operations Research

Or another quantitative discipline

Candidates with strong academic, internship, Kaggle, research, or personal projects in data science and machine learning are encouraged to apply.

What We Look For

More than certifications, we look for candidates who can take a dataset, understand it, ask the right questions, analyse it systematically, build appropriate models, and explain what the results mean.

Candidates should be comfortable learning new techniques and working on problems where the solution may not be predefined.

Internship Exposure

Selected interns will get an opportunity to work on:

Real-world enterprise and client datasets

EDA and automated analytics

Predictive and machine learning models

Forecasting and decision intelligence

GenAI and LLM-assisted analytics

AI agents and intelligent workflows

Production-oriented AI/ML use cases

Strong performers may be considered for longer-term opportunities with the organization.Who can apply:Only those candidates can apply who:are available for full time (in-office) internshipcan start the internship between 29th Sep'26 and 3rd Nov'26are available for duration of 3 monthshave relevant skills and interests* Women wanting to start/restart their career can also apply.Stipend:INR₹ 10,000 - 11,997 /monthDeadline:[hidden]:59:59Other perks:Certificate, Letter of recommendationSkills required:Python, SQL, Statistical Modeling, Machine Learning, Analytical Thinking, Problem Solving, Statistics, R Programming, Effective Communication, Data Analysis, Feature Engineering, Model Evaluation, Probability and Python LibrariesOther Requirements:1. Who have passed masters in DS.About Company:Aaum Research and Analytics, founded by an IIT Madras alumnus, brings extensive global business experience & works with Fortune 100 companies in North America and the Asia Pacific. Established at IIT Madras ecosystem with a focus on researching and devising sophisticated analytical techniques to solve the pressing business needs of corporations ranging from travel, healthcare, entertainment, FMCGs, finance, insurance, retail, and telecom. geniSIGHTS is a subsidiary of Aaum Research and Analytics.

Responsibilities

Selected intern's day-to-day responsibilities include:

We are looking for a hands-on Data Scientist Intern who is interested in solving real business problems using data, machine learning, and AI.

The intern will work on live datasets across domains such as manufacturing, healthcare/pharma, financial analytics, and enterprise decision intelligence. The role will involve exploratory data analysis, feature engineering, predictive modelling, model evaluation, and increasingly, the use of LLMs and GenAI as part of modern data science workflows.

We are looking for someone who is strong in fundamentals, comfortable working independently with data, and willing to experiment, learn, and build.

Key Responsibilities

Clean, preprocess, validate, and analyze structured and semi-structured datasets.

Perform detailed exploratory data analysis (EDA) and identify patterns, relationships, anomalies, and data-quality issues.

Develop and evaluate machine learning models for classification, regression, forecasting, clustering, and other business problems.

Perform feature engineering and identify relevant variables for modelling.

Establish baselines and evaluate models using appropriate statistical and ML metrics.

Use Python and/or R for data analysis, visualization, statistical analysis, and modelling.

Work with SQL and databases to extract and analyze data.

Build clear visualizations and analytical summaries for technical and business users.

Experiment with LLMs/GenAI for activities such as data understanding, automated analysis, insight generation, and model-assisted workflows.

Support development of reusable analytical pipelines and AI/ML components.

Work closely with senior data scientists, developers, and business teams on client and product initiatives.

Clearly document datasets, assumptions, methodology, experiments, models, and findings.

Present findings in a simple and business-understandable manner.

Required Skills

Good working knowledge of Python.

Familiarity with libraries such as:

Pandas

NumPy

Scikit-learn

Matplotlib / Seaborn

Good understanding of:

Statistics and probability

Exploratory data analysis

Regression and classification

Feature engineering

Model evaluation

Basic knowledge of SQL and relational databases.

Ability to understand unfamiliar datasets and independently investigate them.

Strong analytical and problem-solving skills.

Ability to clearly explain analytical findings.

Good to Have

Experience with R and statistical modelling.

Exposure to time-series analysis and forecasting.

Familiarity with XGBoost, LightGBM, CatBoost or similar ML libraries.

Exposure to Generative AI, LLMs, prompt engineering, RAG, or AI agents.

Familiarity with open-source LLMs such as Qwen, Llama, or similar models.

Experience using APIs and integrating AI/ML models into applications.

Familiarity with Git/GitHub or GitLab.

Exposure to cloud environments, Jupyter/JupyterHub, Docker, or similar development environments.

Educational Qualification

Bachelor's or Master's degree in:

Computer Science

Data Science / Artificial Intelligence

Statistics

Mathematics

Engineering

Econometrics / Operations Research

Or another quantitative discipline

Candidates with strong academic, internship, Kaggle, research, or personal projects in data science and machine learning are encouraged to apply.

What We Look For

More than certifications, we look for candidates who can take a dataset, understand it, ask the right questions, analyse it systematically, build appropriate models, and explain what the results mean.

Candidates should be comfortable learning new techniques and working on problems where the solution may not be predefined.

Internship Exposure

Selected interns will get an opportunity to work on:

Real-world enterprise and client datasets

EDA and automated analytics

Predictive and machine learning models

Forecasting and decision intelligence

GenAI and LLM-assisted analytics

AI agents and intelligent workflows

Production-oriented AI/ML use cases

Strong performers may be considered for longer-term opportunities with the organization.