Maths | Data/ML | Fullstack | Nix/NixOS

"Basically a wizard"

Employed at Tweag to build excellent software

• LinkedIn: https://www.linkedin.com/in/guillaume-desforges
• GitHub: https://github.com/GuillaumeDesforges/

I'm a freelancer, contact me by email. Currently not available for freelance.

Lectures

Albert School of Data

Sep 2023 - now: Lecturer

• "Supervised Learning"
• "Scraping and data cleaning"

Ecole des Ponts

Sep 2022 - now: Lecturer

• 1-day course and workshop to version control with git

Sep 2021 - 2023: Teaching Assistant

• "Techniques de dévelopment logiciel"

Experience

Tweag, a Modus Create company

• consultancy: requirement gathering, delivery, communication
• leadership: coaching, project management, group roadmap
• growth: hiring (interviews), marketing (speaker, blog editor), sales (solution design)

Sep 2023 - now: Senior Data & Software Engineering Consultant

• fullstack web development, embed LLM
• build foundation of a marketing engine that uses ML
• build custom ERP integration (DDD, DevOps)

Sep 2021 - Sep 2023: Data & Software Engineering Consultant

• scaffold Python monorepos (blog post)
• distributed cloud computing for ML (dataloader backed by ray on Azure AKS)
• native extension for Spark in Scala (github:kaiko.ai/spark-dicom)
• analysis and processing of temporal geospatial data
• speaker at PyConFr 2023: "Python moderne et fonctionnel pour des logiciels robustes" (video)

May 2020 - Oct 2020: Internship

• "Arrow"-based effect system to author extendable type-safe workflows (github:tweag/funflow, blog post)

Polar Analytics

Jan 2020 - Aug 2021: Data & Software Engineer

• integrate with many third party data sources
• manage ETL jobs, data freshness and data accuracy

Freelance

Jan 2020 - Aug 2020: Fullstack Software Engineer

• React: scaffold and develop
• Spring Boot: models, services, controllers, tests

Jan 2013 - Mar 2013: Backend Software Engineer

• PayPal payment for an online shop

Education

• 2019-2020: Master "Data and Artificial Intelligence", Institut Polytechnique de Paris
• 2016-2020: Ingénieur, Ecole des Ponts

Skills, technologies, opinions

data engineering

• analytics (Hadoop MapReduce, Spark, Modern Data Stack, superset)
• cloud data lakehouse (Spark SQL, BigQuery, Snowflake, Athena)
• parallel computing, distributed computing
• data transformation pipelines need similar features than build systems

data science/ML

• you gotta love a good linear regression (or xgboost)
• aren't Foundational Models just crushing the field?

software engineering

• static typing is a must
• type-hinted Python is nice
• apply FP ideas (Haskell, Scala) to other languages (Python, Rust, Java)
• Inheritance is bad
• Inheritance is bad, really
• Domain Driven Design (DDD) is good
• automated testing matters
• aim for 100% automated deployment
• NixOS is ❤

web applications

• frontend: React is a good default, the Open Web Platform is most stable
• backend: REST is good, most people mean CRUD by REST, GraphQL is nice but complex, RPC is battle-tested
• HTMX is worth knowing

desktop application & mobile development

• make a web app unless you need it offline