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A healthcare analytics company, required a sophisticated synthetic data generation system to support code development and testing within their Medicare claims analytics platform. The technical challenge involved generating approximately 6 million synthetic patients whilst maintaining statistical alignment with real-world distributions, implementing appropriate billing patterns across diverse encounter settings, and creating clinically plausible patient journeys with realistic progression patterns.
Quantitative Outcomes:
Designed system generating 6 million synthetic patients
Implemented persistent patient identity system maintaining demographic consistency across unlimited monthly generations
Created validation framework measuring alignment across prevalence rates, comorbidity patterns, treatment sequences, and billing distributions
Qualitative Benefits:
Enabled realistic code testing environment reflecting true population complexity beyond single-disease scenarios
Provided epidemiologically plausible synthetic data supporting development of sophisticated analytics algorithms
Created foundation for testing correlation analyses between National Provider Identifiers, hospitals, and patient diagnoses
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Designed a modern health biomarkers mobile app that turns complex medical data into a simple, insight-driven experience.
Beautiful UI with clear overview, detailed biomarker trends (Hemoglobin, Albumin, etc.), optimal/sub-optimal indicators, progress tracking, and an AI health assistant for personalized guidance.
Lovely concept
A founder from Silicon Valley came to us with a product that was already working.
The AI logic was solid. The backend did what it was supposed to. DataScoop could analyze metric changes, find the root causes and explain them in plain language - which, honestly, is genuinely hard to build.
The problem was everything the user actually saw.
The interface hadn't kept up with the product. There were Figma designs ready, a completely new visual direction, but no one had translated them into a working frontend yet. The existing code wasn't built to carry them. And the data visualization layer, the part that makes analytics actually readable, needed to be rebuilt from scratch.
So the ask was clear on the surface: rebuild the frontend, implement the new design, make it fast.
In practice, it was more nuanced than that.
Complex data files needed to be parsed and turned into charts that told a story, not just displayed numbers. Performance had to hold up so that insights landed in seconds, not after a loading spinner that made you forget what you were looking at. And all of it had to feel like it belonged together: the AI output, the visualizations, the interface, the Slack and email delivery.
We rebuilt the frontend in React.js and TypeScript, applied strict code quality standards from the start and worked through the Figma designs screen by screen, not just implementing them, but pressure-testing how they behaved with real data.
The hardest part wasn't any single technical problem. It was maintaining coherence, making sure that as the pieces came together, they felt like one product, not a patchwork.
The result is a platform where a data analyst can open it, see a metric move, and understand why, in plain language, in seconds, delivered wherever they actually work.
That's the version DataScoop's users have now.
Check it out
Great job
Project: Velocity SaaS – Revenue Turnaround Analysis
Type: Independent portfolio project
Dataset: Synthetic B2B SaaS, 2,000 accounts / 2.2M rows, benchmarked against ProfitWell/OpenView
What I did:
MRR waterfall analysis, churn segmentation by plan/industry/channel
Usage decay / silent churn risk modeling
Logistic regression churn predictor: 98% recall, 70% precision, 0.82 F1
Built executive Looker Studio dashboard + Streamlit churn predictor
Outcome: $411K silent MRR at risk identified, CS-ready prioritized account list
Stack: SQL, DuckDB, Python, Pandas, Scikit-learn, Looker Studio, Streamlit
Live app: velocity-saas-churn-predictor.streamlit.app
Repo: github.com/SURAJRAJPUT2006
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