Role Overview
As a Machine Learning Intern, you’ll work closely with our founding team on a passion project
focused on developing predictive models that process structured data to deliver meaningful
outputs for users. This hands-on role involves data collection, feature engineering, model
development, and deployment. We’re looking for someone with a strong technical foundation,
enthusiasm for solving real-world problems, and a proven track record in data-driven projects.
Key Responsibilities
● Data Collection & Processing: Source, clean, and normalize structured datasets from
public and proprietary sources, ensuring consistency and quality for model training.
● Feature Engineering: Design and implement features to capture patterns in sequential
data, leveraging domain-specific constraints and contextual relationships.
● Model Development: Build, train, and evaluate machine learning models (e.g.,
regression, ensemble methods) to predict numerical outputs with uncertainty
quantification.
● Validation & Testing: Conduct rigorous testing to ensure model accuracy and reliability,
including error analysis and performance benchmarking.
● Deployment Support: Assist in integrating models into a production environment,
ensuring scalability and real-time performance for user-facing applications.
● Continuous Improvement: Monitor model performance and incorporate new data to
refine predictions over time.
● Documentation & Collaboration: Document code, methodologies, and findings clearly,
and collaborate with the team to iterate on solutions.
Required Skills & Qualifications
● Proof of Work:
○ Proven track record: Proof of work, GitHub repos, Kaggle notebooks, portfolio
projects, or publications demonstrating relevant ML experience is absolutely
necessary
○ No pedigree required: Only your demonstrated skills matter most
● Technical Expertise:
○ Proficiency in Python and libraries like Pandas, NumPy, Scikit-learn, and
XGBoost for data manipulation and machine learning.
○ Experience with regression models, ensemble methods (e.g., Gradient Boosted
Trees), or Bayesian approaches.
○ Familiarity with feature engineering for structured or sequential data.
○ Basic understanding of APIs and model deployment for real-time applications.
● Data Handling:
○ Experience cleaning and normalizing datasets, handling inconsistencies in
naming or formatting.
○ Ability to work with structured data sources like CSVs, databases, or regulatory
filings.
● Problem-Solving:
○ Strong analytical skills to translate domain constraints into model features and
predictions.
○ Comfort with uncertainty quantification (e.g., quantile regression, ensemble
distributions).
● Other:
○ Current enrollment in or recent graduation from a degree program in Computer
Science, Data Science, Statistics, or a related field.
○ Passion for working on innovative, early-stage projects with a small, driven team.
○ Ability to work independently in a fast-paced startup environment.
○ Strong communication skills to document and explain technical work clearly.
What We Offer
● Paid Internship Opportunity.
● Hands-on experience building a real-world ML product as part of a passion project with
high potential for impact.
● Collaborate directly with a founding team of IIT Bombay & IIM Bangalore grads.
● Opportunity to contribute meaningfully to a consumer-facing solution.
● Flexible remote work.
How to Apply
Please submit your resume + links to your GitHub portfolio or relevant projects showcasing
your machine learning and data science work. Highlight any experience with predictive
modeling, feature engineering, or structured data processing.
Submit your applications here : Link
