Nihad Aslanzade
Statistician & Full-Stack Engineer, working across two disciplines that are usually kept apart.
I build production software - Next.js, TypeScript - and the statistical models underneath it, in Python and R. MSc in Statistical Methods and Applications from Sapienza University of Rome. Based in Baku, working with teams remotely.
Ask Me Anything
Grounded in my background - projects, stack, and the statistics behind MyMCMC.
About
I hold an MSc in Statistical Methods and Applications from Sapienza University of Rome (90/110), with a thesis on adaptive MCMC algorithms, a BBA in Economics from the Academy of Public Administration under the President of the Republic of Azerbaijan (95.8/100), and I'm a SABAH.academy alumnus. That background sits underneath everything I build.
Today I work as a System Architect & Full-Stack Engineer, moving between Next.js and TypeScript on the product side and Python, R and SQL on the data side. Most of my tools live in the terminal - I spend more time in a customized shell than in any IDE.
Experience
System Architect & Full-Stack Engineer
Own the core architecture of the student recruitment platform, end to end.
Full-Stack & DevOps Engineer
Built the center's digital management system and its IT infrastructure.
Data Scientist & AI Engineer
Built the data architecture for a B2B hiring platform, integrating AI- and NLP-based analysis models.
Data Scientist & Product Analyst
Product analytics and data science for a real-time artisan booking app.
Sales Analyst
Sales performance analysis.
Economic Analyst (Intern)
Economic analysis internship.
Projects
Student recruitment platform connecting prospective students with international universities.
AI-powered B2B hiring platform. Its pricing model is based on active job vacancies and the total number of applicants processed.
Digital management system and IT infrastructure for a vocational education institute.
Cross-platform app connecting clients with local artisans in real time, with live geolocation matching.
MyMCMC - adaptive algorithms, benchmarked
My master's thesis research: a from-scratch R implementation of the classical and adaptive Markov chain Monte Carlo algorithms, benchmarked against real Bayesian posteriors from posteriordb rather than synthetic targets - three algorithms, seven posteriors, four chains of 100,000 iterations each.
| Algorithm | Mean ESS | Worst R-hat | Unconverged | Mean error (ref SDs) |
|---|---|---|---|---|
| AM | 9,887 | 1.005 | 0 | 0.022 |
| RAM | 9,086 | 1.005 | 0 | 0.023 |
| RWM baseline | 4,238 | 2.400 | 2 | 0.131 |
Stack
Five domains, twenty-one tools, mapped as a pentagonal antiprism.
Education
MSc, Statistical Methods and Applications
Sapienza University of Rome
Thesis: Exploration of Adaptive Algorithms
BBA, Economics
Academy of Public Administration under the President of the Republic of Azerbaijan
SABAH.academy
Alumnus
Certificates and awards
Coursework and programmes completed outside the degrees. Each certificate is the original document, and the ones whose issuer publishes a verification page link straight to it.
Let's talk.
Open to freelance full-stack and data engagements, and to conversations about statistical computing.
Download Résumé ↓