# Maxime Bellevue

> Software Engineer at T Rowe Price

Location: Greater London, England, United Kingdom
Profile: https://flows.cv/maximebellevue

## Work Experience
### Software Engineer - Data and Analytics (Investments) @ T. Rowe Price
Jan 2024 – Present | London, England, United Kingdom

### Senior Data Engineer @ Accelex
Jan 2022 – Jan 2024 | London, England, United Kingdom
Python / PySpark (SparkSQL) / Databricks (AWS) / ArangoDB / GitHub (Actions) / pytest-pycov

### Data Engineer @ Lloyds Banking Group
Jan 2020 – Jan 2022 | London, England Metropolitan Area

### Audit Assistant Manager - Applied Science & Data Analytics @ Lloyds Banking Group
Jan 2019 – Jan 2020 | London, United Kingdom
In a ring-fenced division of 16+ people, I have the role of implementing solutions to facilitate the analysis of large volume (un)structured data.
The bulk of my activities are carried out using Python 3.

I have mainly worked on:

Back-End of an NLP web-application
-------------------------------------
In charge of designing & implementing the class structure which can be split in 3 modules:

-> Data-ingestion
Extract in bulk raw text data from (PDF, DOCX, PPTX) documents, this text data is then sliced and stored in a custom data structure.

-> Preprocessing
Toolkit of functions which performs various transformations on text data (remove stopwords, lemmatization using memoization, bigrams generator...)

-> Text Analysis
Exact Search: Implements SQL Like/Wildcard matching behavior
Semantic Search: Based on Word2Vec implementation from Gensim, word matching via cosine similarity.
Named Entity Recognition: Spacy implementation to retrieve mentions of People, Organization and Geopolitical Entities.
Insights via Top-Words: Generates top N word/document using Term-Frequency Inverse Document Frequency (TF-IDF) Score.

  

FuzzyMatching utility
----------------------
Before: Usage of SQL Like function which shows its limits when trying to match addresses, customer names from various data sources.

Now: Implemented 2 different versions of fuzzy matching
1: fuzzywuzzy package which can be slow when number of comparison > 10e6 (based on string manipulations)

2: sklearn NearestNeighbors (K = 1) + TFIDF Vectorizer (with char trigrams) this solutions reduces significantly processing time and based on cosine similarity (linear_kernel).

Processing time: 10e6 comparisons in 15 minutes (2)

### Senior IT Auditor & Data Analyst @ KPMG Luxembourg
Jan 2018 – Jan 2019 | Luxembourg

### Junior IT Auditor & Data Analyst (Information Risk Management) @ KPMG Luxembourg
Jan 2017 – Jan 2018 | Luxembourg
-> Extensive CAATs user in order to deliver IT audit/advisory missions for Banks and Commercial industries
- Advanced VBA-macro developer
- Caseware IDEA specialist (Advanced Functions, IDEAscripts)
- Clean large set of data using regular expressions
- Database manipulation (in the scope of three-way match procedure audit)

### Full-stack Developer Intern @ Meetic
Jan 2014 – Jan 2015 | Paris Area, France
- Benchmarked new frameworks/technologies (AngularJS, ElasticSearch) in order to develop a web application
- Developed both the front/back end of the web application based on a Symphony2 REST API
- Used NoSQL database (mongoDB) indexed with ElasticSearch
- Collected data from Jenkins jobs triggered by commit and merge requests by writing shell scripts


## Education
### Master of Science - MS in Computer Science
EPITA: Ecole d'Ingénieurs en Informatique


## Contact & Social
- LinkedIn: https://linkedin.com/in/mbellevue

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Source: https://flows.cv/maximebellevue
JSON Resume: https://flows.cv/maximebellevue/resume.json
Last updated: 2026-04-05