Barcelona

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 : _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); <

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