Description

Data scientist depuis maintenant 1 an et demi, riche de 3 ans d'expérience professionnelle dans le domaine de la santé. Ces expériences diverses et variées m'ont permise de devenir experte en analyse de données avec une solide expérience en exploration de données, modélisation statistique, machine Learning et visualisation des données.

Je suis capable d'identifier des modèles, des tendances et des relations dans les données pour fournir des informations exploitables à mes clients. Je suis également capable de communiquer clairement mes résultats et mes recommandations pour aider mes clients à prendre des décisions éclairées. Avec une forte expertise en programmation et en utilisation des outils de science des données, je suis en mesure de fournir des solutions personnalisées pour répondre aux besoins spécifiques de mes clients.

Workplace preferences

Can work on-site

Rennes (up to 50km), Lille (up to 40km), Paris (up to 50km), Montpellier (up to 20km), Nice (up to 20km)

Laboratoire Traitement du Signal et de l'Image (LTSI), / INSERM
Data Scientist
HEALTH AND WELLNESS
February 2022 - February 2023 (1 year)
Rennes, France
Developed novel systems and techniques using Natural Language Processing to analyze unstructured clinical datasets, resulting in a 7% improvement of variant detection accuracy.
Integrated deep learning models for label extraction from unstructured and structured text data with a 99% success rate.
Established text-preprocessing and feature engineering pipelines on 10 million clinical records, improving accuracy by 15%.
Designed and implemented a de-identification process for medical notes to ensure compliance with privacy regulations. Developed and maintained software tools to automate the de-identification process and improve efficiency.
Utilized active learning techniques to improve the accuracy of machine learning models for medical data analysis. Collaborated with cross-functional teams to refine models and optimize performance.
Utilized Prodi.gy as an annotation tool to facilitate data labeling for machine learning models. Developed and implemented annotation guidelines to ensure consistency and accuracy of labeled data.
Developed and maintained software tools to preprocess raw clinical data, including data cleaning and formatting tasks. Collaborated with cross-functional teams to identify and address data quality issues.
Designed and implemented normalization tools to convert unstructured clinical data into a structured format.
Developed and implemented a negation detection tool to identify negated concepts in medical text. Collaborated with colleagues to refine the tool and improve its accuracy.
Development of pretreatment tools for clinical raw data
Development of normalization tools for clinical unstructured data
Negation detection tool in medical text
Python NLP Deep Learning Machine learning Text mining PySpark Analyse de données
CHU de Lille
Data Science Intern
HEALTH AND WELLNESS
May 2021 - September 2021 (5 months)
Lille, France
- Conducted an extensive literature review on various techniques utilized for extracting information from medical unstructured text in the healthcare industry.
- Utilized NLP algorithms and classification models to perform Information Extraction from clinical notes with an emphasis on accuracy and efficiency.
- Monitored and evaluated various applications of word embeddings in healthcare, with a focus on identifying their potential benefits and limitations in the field.
- Employed word embeddings on PMSI data to conduct prediction and visualization tasks, successfully leveraging the power of this technology to provide valuable insights and actionable recommendations.
Python Recherche Data visualisation nlp Machine learning Git Gitlab Spacy Anglais
SANOFI
Data Scientist Intern
BIOTECH
March 2020 - September 2020 (6 months)
91380 Chilly-Mazarin, France
- Proficiently extracted essential data from study protocols by utilizing .docx and HTML handling tools in Python, thereby enhancing efficiency and accuracy in data extraction.
- Stayed up-to-date with the latest technological advancements in Machine Learning within the Clinical Research field, continuously seeking opportunities to leverage emerging technologies for improved outcomes.
- Demonstrated exceptional programming skills by developing a cutting-edge Python App that performs Named-Entity Recognition on Compound, Study, and Dataset names, thereby improving the accuracy and efficiency of data extraction processes.
- Successfully developed an auto-correction tool to be utilized on Dataset names, thus streamlining the correction process and eliminating the need for time-consuming manual checks.
- Leveraged Tensorflow to conduct image analysis on study flow charts, thereby generating valuable insights and actionable recommendations to enhance study design and execution.
Python Spacy NLP Data Engineering Machine learning Gitlab

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Master of Science
ILIS Faculte Ingenierie et management de la Sante
2021
Master, Data Science pour la santé
Licence, Santé environnementale
Université du Droit et de la Santé (Lille II)
2019
Licence, Santé environnementale

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Naima Oubenali

Data Scientist | Data Analyst

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