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RWE Data Scientist

Sanofi -
Barcelona, Barcelona provincia

Solicitar ahora

Detalles del empleo

De 70.800 € a 94.400 € al año

Cualificaciones

Power BI
Derecho
Doctorado
Estructura de datos
R
Inglés
Investigación
Máster
SQL
Machine learning
Deep learning
NLP
GitHub
Graph databases
Python

Descripción completa del empleo

RWE Data Scientist

Location: Barcelona, Spain

About the job

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);
Real-World Data (RWD): Experience with Real-World Data (RWD), demonstrated proficiency in working with diverse real-world data sources, including but not limited to: MarketScan, Optum, TriNetX, IQVIA and STATinMED.
Education: PhD in quantitative field such as Statistics, Biostatistics, Applied Mathematics or related field with 6 years of industry or academic experience; Relevant Master’s Degree, with 10 years of related industry or academic experience.

Soft skills: Integrity; Analytic excellence; Excellent written and oral communication skills; ability to communicate complex topics and data results in a concise and precise manner; High level of self-awareness; Ability to cooperate cross-functionally; Problem-solving and creative thinking skills; Enjoys working independently, flexibly and as part of a team on multiple projects; Enthusiastic and professional approach to working with colleagues, clients, and external collaborators.
Languages: Excellent knowledge of English language (spoken and written)

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