Data Scientist Virtual Internship
Data science hiring tests three things: can you get a messy dataset into shape, can you choose and validate a model honestly, and can you explain what it means to someone who does not code. Your project is generated around those three, for an industry you choose, and a senior data scientist with models in production approves each sprint.
Reviewed by senior data scientists with models deployed in banking and telecomWhat you will work on
- Data cleaning, joins and quality checks on realistic, imperfect data
- Exploratory analysis and visualisation
- Statistical testing and feature selection
- Supervised and unsupervised modelling with honest cross-validation
- Communicating results: a decision memo and a dashboard view
- PRD and HLD approved before coding
Sample project brief
Illustrative — your project is generated for you
Customer churn for a telecom operator.
A prepaid operator loses 4% of subscribers a month. Using 18 months of usage, recharge and complaint records, identify who is likely to churn next month, what drives it, and which retention offer to test first.
- 1
Sprint 1 — Data to insight: merge the three sources, handle missing recharges and duplicate SIMs, and produce an exploratory report with five findings the business did not know.
- 2
Sprint 2 — Model and validate: build a churn model with time-aware validation, report lift over the current rule-based list, and document where the model is weak.
- 3
Sprint 3 — Decide and present: rank subscribers for a retention campaign within a stated budget, estimate expected saved revenue, and present a one-page recommendation plus a dashboard.
Build Across Real-World Domains
FinTech
Financial technology, banking systems, and secure transactions
E-commerce
Online retail, inventory management, and customer experience
Healthcare
Medical data analysis, patient management, and health tech
Telecom
Network optimization, signal processing, and communication systems
Automotive
Autonomous driving, vehicle diagnostics, and smart transport
Manufacturing
Supply chain optimization, predictive maintenance, and IoT
EdTech
Learning systems, student analytics, and gamification
Energy
Smart grid management, renewable tracking, and consumption analysis
This is how you build real experience.
A high-intensity, industry-verified journey from learning to landing.
Choose role
Select your career path and target industry.
AI generates projects
Market-driven scenarios based on real job postings.
Understand problem
Analyze real-world use cases and business requirements.
Build in sprints
Execute your project using milestone-based agile sprints.
AI evaluates
Get instant automated feedback and code analysis.
Human review
A reviewer approves each sprint with written feedback.
Get scored + verified
Earn a certificate with a public verification link, backed by your score.
Show projects
Deploy and showcase your verified work to top companies.
Industry-Verifiable Certificates
Unique Verification URL
Each certificate links to a verifiable public page
Publicly Verifiable Record
Anyone with the link can check the certificate and its score
Score-Based Evaluation
Reflects real performance across all sprints
LinkedIn Ready
You can showcase directly on LinkedIn profiles
Questions about the Data Scientist internship
How much statistics do I need?
Descriptive statistics, hypothesis testing and regression at undergraduate level. The reviewer will push on whether your validation is honest, not on theory.
Which tools?
Python with pandas, scikit-learn and a plotting library, SQL for the data pulls, all in the browser.
How long does it take?
Three sprints per project, two projects for the certificate, around 60–80 hours per project at your own pace.
What does the certificate show?
Score across sprints, reviewer sign-off, project summary and a public verification URL.