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 other disciplines 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 with the 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 advise your 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 expertise and 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 proficiency in 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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