2 Years of Work Experience
AI Engineer | Full-Time (1 year)
Ryte AI leverages big data to drive insights into healthcare efficiency and patient care, with projects spanning from entity resolution to advanced machine learning model development and integration of healthcare data sources. The company focuses on transforming healthcare delivery by developing advanced analytics solutions for both B2C and B2B markets.
Role Summary
As an AI/NLP Engineer at Ryte AI, I led the development and optimization of machine learning models and analytics pipelines, harnessing big data to drive healthcare insights and data-driven decision-making.
Key Tasks
- Advanced Model Development and Optimization:
- Developed extractive and abstractive summarization models using large language models, ensuring diversity and quality across a large corpus of reviews.
- Utilized LightGBM with Hyperopt for hyperparameter tuning, improving model accuracy over baseline.
- Entity Resolution and Data Processing:
- Spearheaded the design and implementation of entity resolution pipelines, reducing processing time from 3 days to 1 hour and increasing F1-Score from 77% to 98% on clinical datasets.
- Scalable Data Transformation:
- Engineered scalable Spark-based data transformation codebases, optimizing performance for datasets exceeding 10 TB, and using Scala UDFs for complex data processing.
- Azure and Cloud Integration:
- Leveraged Azure products, including Azure Databricks, Azure VMs, and Azure Storage Manager, to deploy scalable data processing and machine learning solutions.
- Streamlined workflows using Azure DevOps, enhancing collaboration and deployment efficiency.
- Active Learning and Data Validation:
- Incorporated active learning techniques into the pipeline, improving model adaptability and efficiency through a Streamlit web application with a PostgreSQL backend.
- Implemented Test-Driven Development (TDD) using PyTest and Pydantic, ensuring robust data validation and reliable model performance.
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%.
