Internship programme
Finance & Statistics Internship
Statistics applied to money, with datasets that are not clean.
Textbook finance data arrives tidy. Real data has gaps, revisions, survivorship bias and three different date formats in the same column. This programme puts you in front of the messy version, teaches the statistical methods that hold up against it, and asks you to defend your conclusions to someone who will push back.
Day to day
What you actually do.
- Clean and reconcile real financial datasets end to end
- Build and back-test a forecasting or risk model
- Produce an analyst-grade written report each fortnight
- Defend your assumptions in a review session
- Present findings to a non-technical audience
Structure
10 weeks, phase by phase.
- Weeks 1–2
The analyst toolkit
- Python for data work: pandas, NumPy and the plotting stack
- Excel and SQL for the environments that still run on them
- Descriptive statistics, distributions and what they hide
- Sourcing, validating and documenting a dataset
- Weeks 3–5
Inference and modelling
- Hypothesis testing and confidence intervals, used correctly
- Regression, multicollinearity and reading a residual plot
- Time-series analysis: stationarity, ARIMA and seasonality
- Monte Carlo simulation for scenario work
- Weeks 6–8
Applied finance
- Portfolio statistics, volatility and correlation structure
- Value at Risk, stress testing and their well-known limits
- Credit risk scoring and model validation
- Back-testing discipline and how it goes wrong
- Weeks 9–10
Capstone and communication
- An end-to-end analysis on a dataset you choose
- Dashboard or report built for a decision-maker
- Peer review and a formal defence of your method
- Interview preparation for analyst roles
What you will use
What you leave with
- A portfolio of three written analyses with real data
- A back-tested model with documented assumptions
- AICTE-recognised completion certificate
- Mentor reference letter
- Interview practice for analyst and risk roles
Who this is for
Eligibility, stated
plainly.
We would rather turn someone away at the application stage than three weeks into a cohort. If you are unsure whether you qualify, apply and ask — we answer honestly.
- Students of statistics, economics, commerce, mathematics or engineering
- CA, CFA and MBA candidates looking for applied data work
- Working knowledge of basic statistics
- Around 15 hours a week
Questions
About this programme.
Do I need to know how to code?
Basic Python helps, but the first fortnight covers it from the beginning. What matters more is statistical intuition — being willing to ask whether a result means anything before you make a chart of it.
Is this useful for a CFA or an MBA application?
Yes. Applied project work with real data is one of the few things that differentiates a finance application, and the written analyses you produce are yours to include in a portfolio.
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Applications
Ready for the finance & statistics track?
Send us a CV.
One paragraph about what you want to build is worth more to us than a cover letter template. We read every application and reply to every one.
Email hackedinsolutions@gmail.com · Reply within one working day