2 Years of Work Experience
ML Engineer | Full-Time (1 year)
Contributed to both applied NLP/LLM development (RyteGPT) and traditional ML pipelines in a high-scale healthcare data environment. Built robust data and ML systems on Azure, combining practical engineering with research-driven prototyping.
Key Contributions
- Entity Resolution Pipeline: Led the redesign of patient-provider matching pipelines, improving F1 score from 77% to 98% and reducing processing time from 3 days to 1 hour. Techniques used included fuzzy logic, TF-IDF, and custom heuristics.
- Big Data & Azure Engineering: Developed Spark-based pipelines processing 10+ TB of healthcare data. Used Scala UDFs for complex transformations and deployed solutions with Azure Databricks, VMs, and Blob Storage.
- ML Testing & Reliability: Built robust validation layers using PyTest and Pydantic. Enforced TDD and schema checks across all core pipelines, achieving 100% test coverage in critical services.
- RyteGPT – NLP & LLM Modules:
- Intent Classification: Fine-tuned Roberta on synthetic data for 4-class intent detection (COR, INC, OOC, APPROPRIATE). Deployed to Azure Vertex AI with >90% accuracy.
- Entity Extraction: Deployed LLaMA 3 and Mistral 7B models using vLLM + AWQ quantization. Designed IOB-tag-based evaluation pipeline using spaCy to track token/segment accuracy and F1.
- Entity Linking: Built semantic search over medical terms using BioLORD embeddings + reranking with a cross-encoder. Achieved high top-k precision and improved strict match accuracy using GPT-4-generated variants.
- Dataset Curation: Generated and labeled 1,000+ synthetic medical queries. Collaborated with MDs via Prodigy and GPT for multi-entity labeling (disease, procedure, specialty, location, distance).
- ETL & Integration: Ingested and normalized data from external APIs and sources like PubMed, CMS, AHA, LexisNexis, and ClinicalTrials.gov. Implemented scalable deduplication, mapping, and linking workflows.
- CI/CD & Deployment: Used Azure DevOps for version control and release pipelines. Managed GPU workloads for quantized model inference. Tracked model performance using MLflow and DVC.
- R&D Initiatives: Prototyped new architectures (e.g. contrastive learning for entity resolution, multi-modal queries) and presented findings to product and engineering teams.
Data Scientist GCP | Internship (6 months)
As part of Orange’s AI team, I contributed to automating the classification of French-language customer complaints using a fully cloud-native solution built on Google Cloud Platform (GCP). The goal was to enhance feedback processing while reducing reliance on manual labeling and costly third-party services.
I led a key use case within this migration: deploying an active learning pipeline for NLP classification on GCP's Vertex AI, resulting in improved precision, scalability, and operational efficiency.
Key Tasks
- Designed and deployed an NLP model using Vertex AI AutoML to classify customer complaints, achieving ~86% precision and ~75% recall after iterative improvements.
- Implemented an active learning loop to efficiently manage over 200K unlabeled complaints, significantly reducing manual annotation workload.
- Built a scalable prediction pipeline on GCP (BigQuery + Cloud Functions), processing 1,000 rows in under 2 minutes with structured confidence outputs.
- Led preprocessing using SpaCy's French model for lemmatization, tokenization, and stopword removal, ensuring linguistic accuracy and model robustness.
- Evaluated model performance with detailed metrics (log loss, precision, recall, F1-score) across three training iterations; replaced K-means with active learning due to initial underperformance.
- Collaborated closely with Orange's business team to validate and manually review uncertain predictions, aligning ML outputs with domain-specific terminology.
- Ensured data integrity and traceability by using claim_id as a primary key and integrating results back into BigQuery for visualization and analysis.
- Used GCP tools such as Vertex AI Notebooks, Log Explorer, Cloud Storage, and BigQuery SQL scripts for full-stack ML pipeline orchestration.
Manager Contact
- Name: Benoit Eock Belinga
- Role: Lead Data Scientist | Programme Data / IA
- Email: [email protected]
- Phone: +33 6 84 59 08 70
Recommendation Letter
For a detailed recommendation, please see the letter from my manager:
Data Engineer | Internship (6 months)
As a Data Engineering Intern at Stellantis, I played a pivotal role in the Carflow MEA Dashboards project, a key initiative aimed at leveraging data from diverse sources to monitor supply chain operations in the MEA region. The existing manual ETL (Extract, Transform, Load) process posed challenges in terms of increased effort, potential human error, and inefficiencies. My primary responsibility was to automate this ETL process, thereby streamlining data management for the project.
Key Tasks
- Collaborated closely with a data architect to establish an effective working environment, gaining valuable insights into the data team's operations.
- Conducted in-depth research into Stellantis's Supply Chain business, facilitated by the supply chain business team, to understand the business context and requirements.
- Analyzed the existing ETL solution, identified business requirements, and mapped out a strategic plan for process improvement.
- Designed and implemented an automated ETL solution using PySpark, Apache Airflow, and Oracle Exadata, tools from the Stellantis Data department.
- Conducted rigorous testing of the data pipelines and documented the end-to-end automation process to ensure knowledge transfer and future reference.
- The implemented solution significantly improved the system's efficiency, reducing latency by 46ms and decreasing the failure rate by 82%.
