Barcelona

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Full time

R

RWE Data Scientist

+ _Location: Barcelona, Spain_

About the job

Our Team

Our Team

Sanofi Business Operations is an internal Sanofi resource organization based in India, Spain, Hungary, China, Malaysia & Colombia and is setup to centralize processes and activities to support Specialty Care, Vaccines, General Medicines, CHC, CMO, and R&D;, Data & Digital functions.

Sanofi Business

Operations strives to be a strategic and functional partner for tactical deliveries to Medical, HEVA, and Commercial organizations in Sanofi, Globally.

As RWE Data Scientist you'll provide a high level of expertise in employing cutting-edge analytical & computational approaches to drive evidence-based pharmaceutical product development;

provide scientific and technical leadership in machine learning and AI;

work closely with otherdisciplines across Sanofi including Business Units, Digital, R&D;, Biostatistics, Information Technology Systems and other Data Science partners to deliver cutting edge analysis to key business questions.

Examples of Advanced Analytics activities:

+ Machine/Deep Learning to elucidate disease trajectories, patient subtypes, define underdiagnosed conditions, and unmet health needs;

+ Create a framework for generating re-usable models and insights across big-data (e.G. EHRs, claims) and rich small data sets (e.G. clinical trials, imaging);

+ Generating insights by merging diverse data streams e.G. health, surveillance, trend data, sensor, imaging;

+ Adoption of emerging technology into an analytical framework: distributed analytics, graph databases

People

+ Work together with RWE team to support projects across the franchises motivated by business needs;

+ Work closely withthe Medical, Market Access, HEOR,



and Commercial team to maximize the value of our portfolio of priority assets worldwide;

+ Work collaboratively within Medical and across functions, with clients and external collaborators;

+ Act as a subject matter expert in data science, statistical analysis and/or modelling working on team projects;

+ Work with internal and external study lead to execute Advance Analytics projects and studies

+ Mentor analysts on advanced analytics and RWE techniques;

conduct technical workshops and training sessions

Process

+ Work together and lead research analytical projects, including project conceptualization and design;

+ Lead analysis of healthcare data, including clinical trial datasets, transactional claims, and electronic health records, using established and novel statistical and analytical techniques;

+ Generate rapid response analyses for cross-functional stakeholders;

+ Lead or contribute to drafting and reviewing technical and study reports, manuscripts for publishing in high-impact peer-reviewed journals, and abstracts and presentations for international conferences;

+ Actively manage project activity and timelines;

+ Internally adviseyour colleagues in the Health Economics, Commercial, and Medical franchises on your areas of technical and research expertise as directed by your supervisor;

+ Communicate complex concepts and interpretation of analysis and findings to different audiences, including health economists, clinicians, policy makers, and health systems;





+ Represent the team at external meetings;

+ Validate and secure access to third-party healthcare data-sets

Performance

+ Program, QC, and execute end-to-end RWE studies using diverse real-world data sources (claims, EHR, registries) and standardized formats (OMOP CDM);

implement and execute computational and statistical methodologies in Advanced Analytics for RWE;

+ Provide expertiseand execute advanced analytics for solving problems across R&D;, Medical Affairs, HEVA and Market Access Strategies and Plans

About you

+ Experience xugodme : _8+ years' experience;

_ High level proficiencyin at least two or more technical or analytical languages (R, Python, SQL);

experience with advanced ML techniques (neural networks/deep learning, reinforcement learning, SVM, PCA, etc.) and causal inference methodologies (propensity score methods, inverse probability weighting, doubly robust estimation);

confounding adjustment techniques;

comparative effectiveness research design;

survival analysis (Kaplan-Meier, Cox models, competing risks);

longitudinal data analysis methods;

Strong healthcare data analysis expertise, expertise in data analysis techniques, and good understanding of healthcare datasets (EHR, Claims, RCTs), and data structures;

Expertise in use of statistical methods to investigate real-world problems (e.G., patient journey, time to event analysis);

Advanced experience in preparing, analysing, and managing large healthcare datasets in interventional and/or non-interventional studies;

Ability to prototype analyses and algorithms in high-level languages embracing reproducible and collaborative technology platforms (e.G. GitHub, containers, jupyter notebooks);

Exposure to NLP/LLM technologies and analyses;

Knowledge of some data visualization technologies (ggplot2, R shiny, plotly, d3, Power BI);

📌 Data Scientist - Document Intelligence & Multimodal Ai (Barcelona)
🏢 Capitole
📍 Barcelona