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)
At Orange, we were faced with a complex challenge: our existing data ecosystem was costly and required extensive data engineering services from third-party providers. The solution came in the form of Google Cloud Platform (GCP), which offered a comprehensive suite of services including cost-effective storage, serverless architectures, and robust data engineering tools.
Recognizing the potential of GCP, Orange brought me on board as a Data Scientist to lead this transition and to explore the full range of solutions that GCP could offer. Our initial focus was on classifying customer complaints.
Key Tasks
- Led the transition of Orange's data ecosystem to Google Cloud Platform (GCP) to reduce costs and reliance on third-party data engineering services.
- Developed a machine learning model on Vertex AI to classify customer complaints, improving the understanding of customer feedback and identifying areas for improvement.
- Performed data engineering tasks including establishing the architecture for machine learning solutions on GCP's Vertex AI.
- Conducted extensive data preprocessing and cleaning to prepare data for machine learning model training.
- Selected and tuned machine learning algorithms to optimize model performance.
- Implemented active learning techniques to handle a large amount of unlabeled data, iteratively improving the model's performance.
- Achieved a model accuracy of 91%, demonstrating the effectiveness of the data science methodologies employed.
- Successfully tested and validated the new data architecture, confirming its efficiency and robustness.
- Utilized data science to drive business decisions and strategies, highlighting the importance of data-driven insights in business operations.
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%.
