Data Science & ML Intern

BuildBros Innovations; Bengaluru; Hybrid; INR 112,000 - 201,000; Part time

Job summary

BuildBros Innovations is offering a Data Science & ML Intern position in Bengaluru with remote/onsite flexibility. The role focuses on real-world AI problems, from plant disease detection to business analytics dashboards.

You’ll build ML models, analyze diverse datasets, and deploy AI solutions that make a tangible impact. The internship lasts 3–6 months and provides a stipend of ₹10,000–₹18,000 per month.

Qualifications

• Proficiency with Pandas, NumPy and Scikit-learn for data analysis and modeling.
• Solid understanding of ML fundamentals: bias/variance, overfitting, CV, metrics.
• Familiarity with Jupyter notebooks and data visualization libraries.
• Basic statistics knowledge: distributions, hypothesis testing, correlation.

Responsibilities

• Clean, preprocess, and analyze large datasets using Pandas, NumPy, and SQL.
• Build and train ML models — classification, regression, clustering, and deep learning.
• Create insightful data visualizations with Matplotlib, Seaborn, Plotly, or Streamlit.
• Develop and fine-tune CNN models for image classification (plant disease detection).
• Deploy trained models as REST APIs using Flask or FastAPI.
• Track experiments with MLflow and document model performance metrics.
• Present findings and insights to non-technical stakeholders.

Skills

Pandas

NumPy

Scikit-learn

ML fundamentals

Statistics

Jupyter notebooks

Data visualization

Tools

MLflow

Docker

TensorFlow / PyTorch

Job description

Data Science & ML Intern

Duration 3–6 Months

Mode Remote / Onsite (Bengaluru)

Stipend ₹10,000 – ₹18,000/month

Openings 3

Apply by Rolling basis

About This Role

Work on real-world AI problems — from plant disease detection for Indian farmers to business analytics dashboards. Build ML models, analyze datasets, and deploy AI solutions that make a tangible impact.

A Day in the Life

You start by exploring a new agricultural dataset, cleaning it with Pandas, running EDA visualizations, training a CNN model for leaf disease classification, evaluating accuracy metrics, and deploying it as a Flask API. By month 2, your model is being tested by real farmers through the AgroConnect app.

What You'll Do

• Clean, preprocess, and analyze large datasets using Pandas, NumPy, and SQL
• Build and train ML models — classification, regression, clustering, and deep learning
• Create insightful data visualizations using Matplotlib, Seaborn, Plotly, or Streamlit
• Develop and fine-tune CNN models for image classification (plant disease detection)
• Deploy trained models as REST APIs using Flask or FastAPI
• Track experiments with MLflow and document model performance metrics
• Present findings and insights to non-technical stakeholders

What We're Looking For

• Working knowledge of Pandas, NumPy, and Scikit-learn
• Understanding of ML fundamentals — bias/variance, overfitting, cross-validation, metrics
• Familiarity with Jupyter notebooks and data visualization libraries
• Basic statistics knowledge (distributions, hypothesis testing, correlation)

Nice to Have

• Experience with TensorFlow or PyTorch for deep learning
• Computer vision knowledge (CNNs, transfer learning, data augmentation)
• NLP experience (text classification, tokenization, transformers)
• Kaggle profile with competition entries or published notebooks
• Experience with SQL and relational database querying
• Knowledge of MLOps (MLflow, Docker, model serving)

Tools You’ll Use

Work on production AI products used by real farmers across India

GPU compute budget (AWS/GCP) for model training and experimentation

Mentorship from experienced ML engineers and data scientists

Certificate with detailed project review and model performance report

Co-authorship on any published research papers or blog posts

Career guidance for ML engineering, data science, or research roles

Access to premium datasets, research papers, and learning platforms

Your Growth Path

ML Engineer (1 year) →

Senior ML Engineer / AI Lead (2-3 years)

Interview Process

Total process: ~7–10 days

1. Resume + Portfolio Review 24-48 hours

   Review Kaggle profile, GitHub notebooks, and ML projects.

2. ML Coding Challenge 3-4 hours (take-home)

   Build a model for a real-world dataset — data cleaning, EDA, training, evaluation.

3. Technical Interview 45 minutes

   Discuss ML concepts, your challenge solution, and model design decisions.

4. Fit Call 20 minutes

   Talk about your AI interests, research ambitions, and work preferences.