You'll need to dedicate 8-10 hrs weekly for projects

Data Science Track

Go from a business problem to a working model, not a tutorial notebook

  • Python
  • SQL
  • Matplotlib
  • PyTorch
  • Pandas
  • NumPy
  • Jupyter
  • GitHub
  • TensorFlow
  • Seaborn
  • Scikit-Learn
Join the teamSpeak to a career consultant
Data Science track preview

Programme Overview

This track puts you inside a simulated data science team solving problems that a real business would pay to have solved. Over 12 weeks you complete 4 projects, each anchored to a company scenario in an industry you choose. You will take a raw dataset, frame it as a modeling problem, build and evaluate a model, and explain the result to someone who is not technical but needs to make a decision based on it. By the end, you have a portfolio of working models, documented reasoning for the choices you made, and interview-ready explanations of the business impact of your work

Data Science programme overview

How you will work at Bridge Path

How your workflow and day-to day looks like at Bridge Path

Your mentor

A human mentor opens each project with a short briefing video framing the business problem, and is available for guidance through the project window

Your mentor guiding team

AI stakeholder

A simulated persona (for example, a Head of Data or Head of Product) reviews your model's business impact and asks the kind of skeptical questions a real stakeholder would ask

AI stakeholder persona

Your Cohort

A shared community channel to compare modeling approaches, debug together, and get a second opinion before final submission

Technical reviewer

Your technical reviewer

A second simulated persona (for example, a Senior Data Scientist) checks your model reasoning and assumptions specifically, separate from the business review, mirroring how real teams split business review from technical review

Project cadence

Types of project You Will Build

Below are the types of projects you’ll engage in while at Bridge Path

Customer churn prediction

Building classification models that predict which customers are likely to leave, with a documented business case for acting on the output.

Demand forecasting

Creating time-series forecasting models for planning and operational decisions under uncertainty.

Patient no-show prediction

Predicting operational risk in healthcare settings, including handling imbalanced data and framing ethical interventions.

Fraud detection

Flagging anomalous patterns in claims or transactions and defending the precision/recall tradeoffs to a technical reviewer.

Your 4 Projects at Bridge Path

Projects you can choose from

Customer churn prediction

Customer churn prediction

A fintech company wants to know which customers are likely to leave in the next 30 days so they can intervene early. You build a churn model and present the business case for acting on it. Deliverable: Model plus a written business impact summary. Skills built: Classification modeling, feature engineering, business translation

Demand forecasting

Demand forecasting

A retail company needs a demand forecast ahead of a seasonal sales period to avoid stockouts and overstock. You build a forecasting model and recommend an inventory planning approach. Deliverable: Forecasting model plus a recommendation on inventory planning. Skills built: Time-series forecasting, model evaluation, stakeholder communication

Patient no-show prediction

Patient no-show prediction

A healthcare provider loses money and appointment slots to no-shows. You build a model predicting which appointments are at risk and recommend an intervention strategy. Deliverable: Model plus an operational recommendation. Skills built: Handling imbalanced data, ethical framing of predictive interventions, healthcare-context communication

Fraud detection

Fraud detection

An insurance company wants to flag suspicious claims before payout. You build a fraud detection model and defend its precision and recall tradeoffs to a simulated technical reviewer. Deliverable: Model plus a defended evaluation report. Skills built: Anomaly detection, precision/recall tradeoffs, technical defense of a model

Business Problems You Will Solve

All projects are based on real business problems

Business problems meeting

Every project is built around a real category of decision a business has to make under uncertainty: who is likely to leave, how much of something to stock, who is at risk of missing an appointment, which transaction looks suspicious. These are the exact categories of problem a hiring manager expects a data scientist to have handled, including defending the model's limitations, not just its accuracy score

Industries You Can Choose From

Pick up to 4 for your internship

Fintech & Digital Banking

What your project will look like:

Investigate what drives customers to abandon a digital bank's onboarding journey and build a predictive model that identifies users most likely to drop off

Fintech & Digital Banking

E-Commerce and Retail

What your project will look like:

Investigate what drives customers to abandon their shopping carts and build a model that predicts which customers are most likely to convert

E-Commerce and Retail

Logistics and Supply Chain

What your project will look like:

Investigate what causes delivery delays across a logistics network and build a model that predicts which shipments are most likely to arrive late

Logistics and Supply Chain

Media and Entertainment

What your project will look like:

Investigate what drives viewers to stop engaging with a streaming platform and build a model that predicts which users are most likely to churn

Media and Entertainment

Healthcare and Healthtech

What your project will look like:

Investigate what factors cause patients to miss appointments and build a model that predicts which patients are most likely to miss their next appointment

Healthcare and Healthtech

Insurance

What your project will look like:

Investigate what drives insurance claims to become costly or take longer to resolve and build a model that predicts high-risk claims

Insurance

Job Market Intelligence

Data Science job market intelligence

Fintech, retail/e-commerce, and healthcare currently show the strongest remote hiring demand for data scientists across UK, US, and Canada, with fintech generally offering the highest remote salary bands at entry to mid-level

Excited student

What You Walk Away With

By the end of your journey with us, you’ll achieve the following:

Achievement 1

4 working models tied to real business decisions, documented end to end

Achievement 2

A CV rebuilt around this specific experience gained

Achievement 3

4 STAR-format interview statements, one per project

Achievement 4

A LinkedIn profile positioned as a data scientist with defensible, real project work

FAQs

Frequently Asked Questions

Below are some of the questions we receieve very often

Do I need machine learning experience already?
A basic understanding of Python and statistics is helpful, but the track is structured so you build on that foundation project by project, starting with simpler models and progressing to more complex ones.
Which modeling libraries will I use?
You will work primarily with Scikit-Learn for classical models, with exposure to TensorFlow and PyTorch depending on the project. The focus is on choosing the right tool for the problem, not on mastering every library.
How is the technical review different from the stakeholder review?
The stakeholder review focuses on whether your model's output translates into a credible business decision. The technical review is a separate session where a simulated Senior Data Scientist checks your modeling assumptions, feature choices, and evaluation methodology.
Is this the same as a normal internship?
While it provides real project experience and simulated business feedback, it is a structured, project-based track designed to build your portfolio and interview readiness.
Join the teamSpeak to a career consultant