Freelancer profile translated to English.
Back to original languageAbout Dex Farrell
Statistician and Web Developer with over 8 years of experience, I design and industrialize data and ML solutions, develop scalable web APIs and applications, and deploy reliable systems on cloud and servers:
Data Science & ML:
- data exploration,
- visualization,
- modeling (Scikit-learn, PyTorch, TensorFlow, Azure Databricks)
MLOps:
- automated pipelines (Airflow, Kubeflow),
- model lifecycle management (MLflow, DVC),
- deployment (Seldon Core / KServe),
- CI/CD,
- monitoring
Web & API Development:
- Front-End (React, Bootstrap),
- Back-End (Python FastAPI/Django, PHP Laravel),
- API and service integration (Mobile Money, Airtime, Data)
Cloud & Infrastructure:
- AWS, GCP, Azure,
- Kubernetes, Docker,
- Ubuntu/CentOS servers,
- Apache/Nginx
I provide turnkey solutions, from design to production, combining data science, MLOps, and web/cloud development.
French
Native or bilingual
English
Fluent
Can work on-site
Lille (up to 50km), Paris (up to 50km), Montpellier (up to 50km), Rouen (up to 50km)
Experience
- Crédit AgricoleMachine Learning Engineer (NBO Project)BANKING AND INSURANCEFebruary 2024 - December 2025 (1 year and 10 months)Roubaix, FranceMigration and optimization of propensity models
- Updating and optimizing propensity models while ensuring result consistency.
Creation of pre-targets- Developing an algorithm to distribute populations by product according to their propensity and relational segmentation criteria.
Codebase optimization- Migrating scripts from Pandas to Snowpark to improve performance and reduce model processing times.
Hyper-personalization- Building segmented test populations to generate reusable data for machine learning models.
Building a Web Store- Setting up a Web data store to enrich propensity models and improve their performance.
- IRCEMData ScientistBANKING AND INSURANCENovember 2022 - December 2023 (1 year and 1 month)Roubaix, FranceQuotation conversion prediction
- Design and implementation of a predictive model to estimate the probability that a prospect will subscribe to a contract based on a quote.
- Objective: improve the conversion rate for funeral contracts by relying on reliable predictions.
Fraud Project – ML Audit and Evaluation- Audit of fraud detection procedures and tools within the Ircem Group
- Evaluation of the performance of an ML model in production since 2018, identification of strengths and weaknesses, and recommendations for improvement to optimize fraud detection.
- CofidisResearch EngineerBANKING AND INSURANCEJanuary 2019 - June 2022 (3 years and 6 months)Villeneuve-d'Ascq, FranceData Analyst: Synergie (COFIDIS Group)- Data extraction and exploitation (Cleaning, statistical analyses...)- Implementation of activity monitoring reporting- Optimization of data analysis processes- Analysis of business needs and issues- Machine Learning and statistical modeling,- Facilitation of a Fraud decision-making committee- Decision-making referent (Facilitation of a decision-making committee on data exploitation for fraud detection, participation in PoCs on Data science solutions...)
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Education
- Master in Statistical & Actuarial EngineeringAix Marseille University2017L’objectif du parcours IMSA est de former des étudiants qui auront à l’issue de la formation la double compétence en mathématiques appliquées et actuarielles (statistiques, actuariat, mathématiques financières, cryptographie, analyse d’image et optimisation) et en informatique (maîtrise de logiciels, programmation, gestion de bases de données et gestion de réseaux, analyse d’algorithmes). Les compétences acquises dans cette formation permettront aux étudiants d’être opérationnels au sein des entreprises du secteur bancaire, de la finance et de l'assurance. Un étudiant du parcours IMSA sera capable de programmer des nouvelles techniques, de concevoir des outils statistiques et informatiques d’aide à la décision, de mettre en œuvre des innovations, mais aussi de gérer des projets au sein de l’entreprise.
Certifications
- MLOps Fundamentals (CI/CD/CT Machine Learning Pipelines)Udemy2022
- Spark Python and Machine LearningUdemy2022