VaibhavMangroliya
Python Engineer • M.Sc. Mathematics • Quant-finance background
I write Python that goes to production. Right now that means a data-validation pipeline at LIST that gates Luxembourg's national environmental database. Before that, almost two years at India's National Stock Exchange — NAV calculation and XBRL parsing for 2,700+ listed companies.
Luxembourg 🇱🇺 · Open to relocation
Shipping production Python at LIST · Esch-Belval
The thread?Production-grade Python.
Same engineering discipline either way — environmental data today, fund-reporting code tomorrow.
My journey
I’m doing an M.Sc. in Mathematics at the University of Luxembourg, six months into a compulsory internship at the Luxembourg Institute of Science and Technology (LIST). The work is real Python engineering: pandas, pytest, REST APIs, GitLab merge requests. The pipeline I work on gates Luxembourg’s national environmental time-series database — if my code is wrong, bad data goes upstream.
Before Luxembourg, I was at India’s National Stock Exchange for almost two years. I wrote the Python that did NAV calculation across the Fair Value Hierarchy (Levels 1, 2, 3) and the XBRL parser that handled financial statements for 2,700+ listed companies. That job is where I learned what reporting code looks like when regulators read the output.
Different domains, same job: Python that holds up in production. The math I do at university and the quant-finance ground I’ve already covered both feed into the same engineering practice — and they map cleanly onto investment-fund reporting work.
Looking for
Availability
Available from autumn 2026, once my current LIST internship concludes. Open to next steps in Luxembourg and across the EU.
Production Python Engineering
pandas, NumPy, pytest, ruff, REST APIs (requests), library/module design. Drove order-of-magnitude speedups on a production pipeline by vectorising every check module in pandas. CI workflows, GitLab merge-request review, performance profiling.
Quantitative Finance & Risk
NAV computation, Fair Value Hierarchy (Level 1/2/3), UCITS framework. VaR (Parametric, Historical, Monte Carlo), Expected Shortfall (CVaR), options pricing & the Greeks, GARCH. Derivative instruments (Options, CDS, CDO/CLO).
Data Systems & Reporting
SQL (Oracle, PostgreSQL), schema design — I designed a 23-table normalised XBRL schema serving 2,700+ companies. ETL pipelines, data quality & validation, time-series analysis, automated regulatory reporting.
How I work
My standard is simple: code I won’t be embarrassed by in six months. At LIST that has meant order-of-magnitude speedups across the validation pipeline, eliminating CI hangs by hardening every external API call, and designing and shipping a new Seasonal Check module built on per-(month, hour) statistical bands. At NSE it meant a normalised 23-table SQL schema that cut data errors by 40% — instead of another fragile Excel workflow.
I also teach the things I learn. My YouTube channel has 290K+ views — Assembly, Engineering Physics, admissions guides. Explaining work to a non-expert audience is the same skill you need to write good client-facing reports.
Stack &expertise.
What I actually use day to day, plus the quant-finance and math ground I have behind it.
Python & Engineering Practice
Quantitative Finance
Regulatory & Products
Data & Databases
Tooling & Other Languages
ML & Scientific Computing
Where I’veshipped.
Two roles, one habit: write Python that other engineers — and regulators — can read.
Research Intern — ENVISION Unit (LEO Observatory)
InternshipCurrentLuxembourg Institute of Science and Technology (LIST)
- Contributing to DataQA, a production Python pipeline (pandas, pytest, ruff, KiWIS REST API) that validates Luxembourg’s national environmental time-series data before re-import into the KISTERS WISKI production database. 30+ merge requests shipped in ~1.3 months — feature, refactor, and bug-fix work — all reviewed via GitLab.
- Designed and shipped a new Seasonal Check module using per-(month, hour) statistical bands derived from historically validated data, with on-disk threshold caching. Extended the validation suite to a new parameter (Global Irradiance) across three existing checks.
- Vectorised every
iterrows()loop across all check modules using pandas — order-of-magnitude speedups on the production pipeline. Removed NumPy as a direct dependency in favour of pandas-native equivalents. - Built a second tactical system, rank-correlating-stations, producing per-parameter Pearson-correlation rankings of national weather stations to feed DataQA’s spatial-consistency check. Identified and fixed a glob-collision bug silently mixing 81 precipitation files into temperature analysis.
Student Research Assistant — Department of Mathematics
Part-timeUniversity of Luxembourg
- Prepare technical documents and research materials using LaTeX for faculty use.
Associate Systems Analyst
Full-timeNational Stock Exchange of India (NSE)
- Developed a Python-based NAV calculation tool automating Fair Value hierarchy classification (Level 1/2/3 assets), asset-liability aggregation, and Net Asset Value computation from Oracle database — directly applicable to investment-fund valuation and reporting.
- Built an XBRL parsing system transforming unstructured financial-statement data (Balance Sheet, P&L, Cash Flow) into a normalised SQL schema (23 tables). Reduced data errors by 40% and enabled automated validation across 2,700+ listed companies.
- Built Java / Spring Boot regulatory-compliance web applications enabling NSE’s compliance team to process SEBI filings, replacing manual Excel-based workflows with automated, audit-ready pipelines.
Academicbackground.
University of Luxembourg
M.Sc. in Mathematics
Mathematical Modelling & Computational Sciences
09/2024 – Present
Vidyalankar Institute of Technology
B.E. in Electronics & Telecommunication
Grade: 1.4 — Top 2% in department
08/2018 – 05/2022
Certifications.
Click any card to expand and see the full topic coverage.
Selectedwork.
Things I built because I wanted to understand them properly. Code, math, and finance — usually all three.
Explore the Quant Lab
Live pricing tools, simulation notebooks, and quantitative finance experiments — running in your browser.
What people say.
“Vaibhav consistently stood out as a sharp and dependable professional. He showed a high level of ownership in his work, often handling critical modules with minimal guidance. Beyond his technical skills, Vaibhav is a collaborative team player with a professional attitude. I confidently recommend him for roles that require strong analytical thinking and problem-solving ability.”
Rahil Kamani
National Stock Exchange of India • 7.1 yrs exp.
Let’s talk.
Best for investment-management reporting, quant internships, or Python data-engineering roles. I read every message.