Data Scientist

• Drive and maintain the data catalog to ensure reliable, up-to-date documentation
• Identify and inventory the company's strategic data
• Define and maintain business, functional, and technical metadata
• Map data flows and maintain data lineage
• Document Data Products and contribute to the dissemination of governance standards
• Define and implement data quality rules
• Build data quality monitoring metrics and manage their evolution
• Develop continuous improvement plans for data quality
• Support data compliance with regulatory requirements and internal policies
• Participate in data projects to integrate governance requirements from the design phase
• Lead workshops with various stakeholders
• Produce governance reports and Data maturity indicators
• Ensure knowledge transfer and promote a data-driven culture within teams

Data Manager

• Industrialization of the integration system: Lead the full migration to the Seyna application, eliminate ad-hoc manual steps, and ensure end-to-end operational scalability.
• Quality & speed standards: Define and maintain standards for bordereaux integration (data correction, writing acceptance tests), explicitly arbitrating trade-offs between quality and deadlines.
• Team management: Structure and lead a team of 3 Data Integration Managers — run operational rituals, allocate scopes, coach and upskill team members.
• Relationship with Tech: Manage the relationship with Tech teams to prioritize Seyna application enhancements and prevent reverting to ad-hoc solutions.
• Business continuity: Ensure continuity across each portfolio by enforcing documentation as a non-negotiable standard and anticipating critical dependencies.
• Service relationships: Structure the relationship with consumer teams (Actuarial, Accounting, Portfolio Owners, Client Performance) to align data with real business needs.
• Cross-functional projects: Lead cross-functional initiatives with other teams (CSM, Legal, Tech...) and continuously arbitrate between speed, quality, workload and business expectations in a high-pressure operational environment!

Manager – Data Integration

• Lead, manage, and motivate an on-site team of annotators working on technical annotation, owning daily team coordination and overall performance
• Run daily operations: task progress and completion tracking, shift coordination, and follow-through so that assigned work is finished and nothing falls through the gaps
• Own annotation quality end-to-end: make sure the standard operating procedures and guidelines are followed, run calibration sessions and targeted refresher trainings, monitor acceptance and rejection rates against targets, and lead root-cause analysis and corrective actions on recurring errors
• Act as the operational point of contact with the client's quality and engineering teams: align on guidelines and specifications, manage the rework loop, and escalate ambiguities or contradictions with clear, documented evidence
• Design, implement and maintain daily quality and productivity tracking and reporting, and provide clear, honest status to the client and DataForce’s program management team
• Interview, test, onboard, train, coach, and develop annotators, and manage individual performance and feedback
• Keep the team aligned as tools, guidelines, and the rating scale evolve, translating changes into clear and consistent instructions across shifts
• Ensure full adherence to confidentiality and data handling requirements
• Coordinate internally with DataForce’s operations and account management team to continuously improve our client services and experience

Team Lead – AI Data Annotation

• Le Data Scientist conçoit et met en œuvre des modèles analytiques et d’intelligence artificielle
• Explorer et analyser des jeux de données complexes
• Identifier des patterns, corrélations et leviers de performance
• Produire des analyses statistiques pour extraction d’insights
• Concevoir des modèles de Machine Learning et Deep Learning
• Adapter les modèles aux cas d’usage métier
• Mettre en production les modèles
• Assurer le monitoring et le maintien des performances
• Travailler avec les métiers pour identifier les cas d’usage
• Traduire les besoins métier en modèles analytiques
• Concevoir des cas d’usage sur IA générative
• Proposer des solutions innovantes
• Collecter, nettoyer et préparer les données

• Caractériser et proposer une rationalisation des bases de données Produits utilisés par l’équipe OSM et au-delà
• Soutenir toutes les mises en place de Key Value Indicators/Attributes, Key Performance Indicators sur les initiatives de développement produit stratégiques
• Soutenir les réflexions et la stratégie des Opérations sur la continuité numérique
• Être un acteur du changement de l’équipe OSM dans son usage quotidien de la data et du numérique

Ingénieur Product Data Manager

• Lead structured data operations end-to-end, ensuring timely and compliant submissions to Health Authorities worldwide.
• Manage and develop a team of structured data specialists to meet all compliance requirements and quality standards.
• Drive the global implementation of IDMP standards and monitor changes to regulatory data requirements.
• Represent Sanofi in working groups with Health Authorities and industry partners to shape regulatory innovation.
• Oversee data quality control and track Key Performance Indicators (KPIs) to ensure on-time submissions.
• Set up and monitor service agreements with vendors and contractors to ensure operational excellence and timely delivery.
• Provide expertise during audits and support inspection processes with required documentation and responses.
• Work with stakeholders to continuously improve structured data processes and build team expertise.

Submission Lead – Structured Data

• Operate at the intersection of data engineering, clinical science, and partner collaboration
• Participate directly in technical conversations with external partners (hospitals, research institutions, CROs/CMOs)
• Translate ambiguous source data into harmonized, AI-ready assets
• Map and align diverse clinical data to industry-standard biomedical ontologies with an emphasis on clinical oncology and immunology data
• Design, build, and maintain data dictionaries, schemas, and metadata models
• Establish, automate, and enforce data quality control (QC) and validation frameworks
• Write production-grade Python code to automate data cleaning and harmonization tasks
• Understand how clinical data is generated in real-world settings
• Actively audit data to find missing variables, anomalies, and hidden biases
• Recognize important data in clinical trials related to oncology/immunology

Senior Clinical Data Manager

• Build from scratch and lead the Data Labeling team (hiring, coaching, and performance management)
• Define annotation guidelines, quality standards, and evaluation frameworks
• Develop quality assurance processes, calibration sessions, and auditing systems
• Partner with AI researchers and engineers to translate research objectives into labeling workflows
• Prioritise labeling projects based on business and research needs
• Monitor operational metrics including quality, consistency, throughput, and cost
• Improve annotation tooling, automation, and workflow efficiency
• Lead complex AI evaluation projects, including safety, preference ranking, RLHF, policy evaluation, and benchmark creation
• Analyse disagreement patterns and edge cases to improve guidelines and model performance
• Manage vendor relationships and ensure consistent quality across distributed teams
• Build reporting dashboards and communicate operational insights to leadership
• Foster a culture of continuous improvement, accountability, and operational excellence

Head of Data Labeling

• Create, modify and maintain product master data (Master Data)
• Ensure data quality, consistency and completeness
• Define and enforce data quality rules
• Identify, analyze and correct anomalies
• Implement metrics to monitor data quality
• Participate in continuous process improvement
• Administer product repositories in SAP and the PIM
• Participate in the deployment of EDI flows
• Participate in digital transformation projects
• Develop dashboards and reports

Product Data Manager

• Identify and define data science use cases aligned with business needs.
• Perform exploratory analyses to extract value from data.
• Design and develop machine learning models tailored to the problems.
• Operationalize and deploy solutions into production environments.
• Monitor model performance and drive continuous improvement.
• Collaborate closely with technical and business teams to ensure solution impact.

• Grow your team: one-on-ones, goal setting, and skills development.
• Recruit and structure the team to reach the target of one data lead per business unit.
• Project management & business-facing role: turn business needs into actionable data projects.
• Help optimize paid acquisition, lead scoring, and management of commercial performance.
• Hands-on involvement in critical projects: dbt/BigQuery modeling, ML lead scoring, Airflow orchestration.
• Documentation, cataloging and traceability: make data discoverable for everyone, with data quality monitoring.

Lead Data

• Réaliser une cartographie des données
• Concevoir un système de gestion des métadonnées
• Identifier et cartographier les sources de données
• Définir un modèle de métadonnées
• Mettre en place un prototype de gestion des métadonnées
• Explorer des approches IA pour automatiser l’enrichissement
• Tester des cas d’usage

Apprenti Ingénieur Data Scientist – INFRA, STF

• Manipuler des bases de données, cleaning, avec l’utilisation d’outils adaptés comme Python et/ou Excel.
• Analyser des données et réaliser des modèles d'économétrie, statistiques bayésienne etc.
• Analyser et construire la stratégie marketing et optimisation media en s’appuyant sur les modèles
• Participer également à la construction des livrables et à la présentation des recommandations à nos clients

Business Data Scientist – Marketing Effectiveness

• Overview all the data related tasks
• Ensure all the recordings exist for each component
• Collect and organise the data in an easily accessible way
• Manage data distribution on multiple sites
• Be aware of data recording restrictions by country
• Manage annotations either by creating tools or organising campaigns
• Extract meta data from datasets
• Help prepare the data for more sophisticated ML models

• Design predictive models (scoring, Next Best Offer, segmentation)
• Leverage multi-source data
• Productionize associated data processing and models
• Analyze customer journeys and conversion funnels
• Implement measurement frameworks (A/B testing, uplift, customer value)
• Provide ROI-oriented recommendations
• Define data science best practices
• Collaborate with business, analytics and IT teams to ensure reliable data and usage.

Data Scientist – Experienced

• Identifier, cadrer et formaliser des cas d’usage Data Science
• Proposer et développer des POCs innovants
• Concevoir et entraîner des modèles de Machine Learning / Deep Learning
• Traiter et structurer des données complexes, parfois textuelles ou non structurées
• Collaborer avec les Data Engineers pour l’industrialisation des pipelines
• Participer aux activités R&D IA de Keyrus

• Design and build multi-agent orchestration systems
• Collaborate with senior data scientists and AI experts
• Produce runnable code to be reviewed by data scientists

Internship – Agentic AI for Semi-Automating the Data Science Workflow

• Develop AI use cases
• Design machine learning models
• Develop use cases around LLMs and generative AI
• Build and optimize data pipelines
• Automate operational KPIs
• Oversee Data and AI processing in production
• Analyze model performance and continuously improve
• Identify bottlenecks and manage ticket reassignments

Data Scientist jobs in France

See all jobs on JobTailor

Data Manager

Manager – Data Integration

Team Lead – AI Data Annotation

Data Scientist

Ingénieur Product Data Manager

Submission Lead – Structured Data

Senior Clinical Data Manager

Head of Data Labeling

Product Data Manager

Data Scientist

Lead Data

Apprenti Ingénieur Data Scientist – INFRA, STF

Business Data Scientist – Marketing Effectiveness

Data Manager

Data Scientist – Experienced

Data Scientist

Internship – Agentic AI for Semi-Automating the Data Science Workflow

Data Scientist