Portfolio Examples · Data Scientist Portfolio
Data Scientist Portfolio Example: A Real Applied ML and Production Deployment Portfolio for 2026
This page is a working example of a real data scientist portfolio website — built on the same Pulse template a paying Seera client would publish. The layout, animations, and styling are byte-for-byte identical to what you would get if you published a data scientist portfolio on Seera tonight. The only difference is the data: instead of a real practitioner's projects, this example uses a fictional but realistic profile (Ravi Krishnan, a Berlin-based senior data scientist working on applied ML in healthcare with prior stops in fintech and e-commerce).
What you can take from this data scientist portfolio example: the section structure, how to present a production-deployed model alongside open-source work, what depth a fraud-detection or recommendation case study should have, and how a senior IC differs in framing from a research-leaning data scientist.
View the Live Data Scientist Portfolio →
The data scientist portfolio at a glance
| Data Scientist | Ravi Krishnan (fictional, modeled on a working pattern) |
| Specialty | Senior IC · applied ML · production deployment |
| Industry | Healthcare, fintech, e-commerce |
| Location | Berlin, Germany — open to remote (EU/UK) |
| Years working | 8 years |
| Portfolio template | Pulse — animated cyan-violet on deep navy |
| Sections shown | Hero · About · Stack · 4 Project Case Studies · Experience · Education · Testimonials · Contact |
Why this layout works for a data scientist portfolio
The Pulse template paired with a cyan-violet palette is a deliberate choice for an applied data scientist portfolio. Three things about why it works:
- Animated, data-vibe aesthetic. Subtle motion on the project cards and headings echoes the dashboard work without going full developer-terminal.
- Strong typography for case-study depth. Data science case studies are technical — model architecture, sample sizes, AUC, A/B significance — and the typography needs to hold them clearly.
- Premium feel signals senior-IC-grade work. Hiring managers reading the portfolio for staff and principal roles are reading visual cues as much as numbers.
Section-by-section breakdown of the data scientist portfolio
1. Hero — role, focus, and current availability
The hero reads "Senior Data Scientist · Applied ML & Production Systems" — specific enough that an engineering manager hiring for an applied-ML role knows immediately whether to keep reading.
2. About — operating style, not biography
The about section explains the operating style: production-and-applied, selective project list, and quarterly deep-dive technical writing. Crucially, it shows current availability above the fold.
3. Tools and methods — grouped by use case
For any data scientist portfolio, this section answers two practical questions: do they ship in the languages and tools we use, and do they understand the production layer underneath. The example breaks the stack into Specialties, Languages & Modelling, Production & MLOps, and Practice. The Production & MLOps grouping is what separates a senior IC from a research-only data scientist.
4. Project case studies — depth over breadth
The case-study section is the strongest part of any data scientist portfolio. The example here shows four projects across deliberately different categories:
- A production-deployed clinical triage model in 12 hospitals (AUC 0.91, full bias audit)
- A fraud-detection embeddings model at a fintech (-41% false positives, A/B tested)
- An open-source uplift-modelling library on GitHub (1.8k stars, MIT-licensed)
- An e-commerce recommender system (+12% homepage CTR, A/B tested)
A data scientist portfolio of four Kaggle competitions shows you can do Kaggle competitions. A portfolio of four different kinds of work — production-deployed, A/B-tested in production, open-source, and applied recommender — shows a data scientist with range that maps to senior and staff roles.
5. Work history — companies, roles, specific contributions
Each role on this data scientist portfolio leads with the most concrete thing the practitioner did there: models shipped, false positives reduced, AUC achieved, juniors mentored.
6. Testimonials — from CMOs, heads of risk, and open-source maintainers
Three testimonials in this data scientist portfolio: a CMO (the healthcare client), a former Head of Risk (the fintech client), and an open-source maintainer (independent verification of the library work).
7. Contact — one email, one clear next step
The contact section is intentionally simple: an email, GitHub, LinkedIn, and a clear note on availability.
What this data scientist portfolio gets right (and what to copy)
- Production-deployed work, leading. The clinical triage model — deployed in 12 hospitals, monitored, audited — is the strongest possible signal.
- Open-source work treated as headline, not side note. The uplift library has its own case study with stars, benchmarks, and adoption.
- Experimental rigour stated explicitly. "Pre-registered A/B test in production over 60 days" — this is what separates a working data scientist from a notebook tourist.
- Bias audit mentioned upfront. The healthcare project explicitly mentions a bias audit by ethnicity, age, and gender — credibility-building for sensitive deployments.
- Range of categories. Healthcare, fintech, e-commerce, open source — signals what the data scientist can be hired for.
How to build a data scientist portfolio like this for yourself
- Upload your CV or data scientist bio to Seera. The AI extracts your projects, models, and stack.
- Pick the Pulse template (or browse the other 15 templates — DevTerminal for research-heavy, Glass for industrial, Aurora for premium analyst).
- Replace the four sample case studies with three to five of your own — at minimum one production-deployed model, one open-source piece, one A/B-tested project.
- Write proper technical case studies. Each project needs model architecture, training data, experiment design, result, and post-launch monitoring or limitations.
- Get three named testimonials — a manager, a peer or open-source contributor, and a downstream stakeholder (clinician, risk manager, PM).
- Connect your custom domain on Pro.
ravikrishnan.comsignals seriousness in a way a builder subdomain never will.
Frequently asked questions about data scientist portfolios
What does a good data scientist portfolio look like in 2026?
A strong data scientist portfolio leads with applied projects that shipped to production — three to five case studies with the model, the experiment design, the deployment architecture, and the post-launch monitoring. Avoid: long lists of competitions, ten notebooks without deployment context.
What sections should a data scientist portfolio include?
Six core sections: (1) Hero; (2) About — operating style; (3) Project case studies — three to five applied ML projects with measurable outcomes; (4) Tools and methods — modelling and MLOps grouped by use case; (5) Work history; (6) Testimonials and education.
What projects should a data scientist include?
Aim for one production-deployed model, one open-source contribution, one A/B-tested project, and one classic applied piece (recommender, fraud, churn). Junior practitioners should lead with two end-to-end projects deployed somewhere.
Which template style works best for a data scientist portfolio?
The Pulse template (animated, cyan-violet, data-vibe) is a strong fit. Research-heavy or paper-led practitioners may prefer DevTerminal (terminal aesthetic) or Glass (dark glassmorphism).
How do I write a data scientist portfolio bio?
Answer four questions: what you do, the company type, the operating style, and current availability. Avoid: "passionate about data", "data wizard", "machine learning enthusiast".
How important is open-source work for a data scientist portfolio?
For senior and staff roles, open-source work is one of the highest-leverage signals — independently verifiable, survives company-confidentiality issues, and shows collaboration with strangers. Even one well-documented library outweighs a longer list of company projects you cannot talk about.