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Real-World Evidence Data Scientist - Job Opportunity at Sanofi

Berlin, Germany

Full-time

Senior

Posted: July 12, 2025

Hybrid

EUR 85,000 - 120,000 per year, with potential for higher compensation based on PhD qualification and extensive RWE experience. Berlin's pharmaceutical sector offers competitive packages, and Sanofi's global presence typically includes performance bonuses and comprehensive benefits that can increase total compensation by 20-30%.

Benefits

Access to cutting-edge real-world data analytics platforms and technologies, positioning professionals at the forefront of healthcare innovation

Opportunity to work with international subsidiaries and global matrix teams, providing extensive cross-cultural collaboration experience

Exposure to multiple therapeutic areas including transplant, diabetes, and cardiovascular medicine, offering diverse specialization opportunities

Partnership with digital organization and advanced analytics teams, enabling skill development in emerging AI and machine learning technologies

Direct impact on patient outcomes through evidence generation that influences regulatory and access decision-making processes

Key Responsibilities

Lead end-to-end execution of real-world evidence studies that directly influence regulatory approval processes and market access decisions, serving as the critical bridge between raw healthcare data and actionable medical insights

Design and implement advanced analytics solutions using machine learning, deep learning, and artificial intelligence to transform complex healthcare datasets into evidence that shapes treatment protocols for millions of patients globally

Establish and maintain data integrity standards across international pharmaceutical operations, ensuring compliance with global regulatory requirements while enabling innovative research methodologies

Collaborate with cross-functional global brand teams to translate business-critical medical questions into robust analytical frameworks that support multi-million dollar product development and market strategy decisions

Serve as the primary technical expert for advanced statistical modeling and causal inference methods, directly advising senior leadership on evidence-based strategies that impact patient care worldwide

Drive the development of next-generation analytics capabilities including generative AI applications, positioning the organization as a leader in pharmaceutical data science innovation

Manage complex analytical projects concurrently while coordinating with external service providers, ensuring timely delivery of evidence that supports critical business milestones and regulatory submissions

Requirements

Education

Master's degree in quantitative fields such as statistics, applied mathematics, computer science, or related field. PhD is preferred

Experience

Expert knowledge in RWE, pharmaco-epidemiology, health outcomes research, statistical methods, etc. Experience working with routinely collected data (claims databases, electronic medical health records, registries and various structured and possibly unstructured sources in the healthcare sector within pharmaceutical company settings. Demonstrated experience in the use of advanced analytics methods. Extensive experience in statistical modelling, causal inference methods (propensity scoring techniques, comparative effectiveness analysis, etc) and knowledge of advanced artistical techniques (e.g GAMs, machine learning, deep learning). Experience working in multiple therapeutic areas – experience in transplant, type 1/2 diabetes or cardiovascular highly preferred. Experienced working in complex global matrix teams and with service providers requiring cross-functional collaboration and alignment

Required Skills

Experience in R, Python or data base programming (SQL) is a must Advanced programming and statistical computing software skills, expertise with core data science languages (predominantly Python, and nice to have R & Scala), experience working with Snowflake and other different database systems (such as SQL, NoSQL) Expertise within some of the following areas: supervised learning, unsupervised learning, deep learning, reinforcement learning, federated learning, time series forecasting, Bayesian statistics, optimization Expertise in RWE study designs and methodologies, including innovative techniques Ability to translate complex technical language into easy-to-understand communication with collaborators and stakeholders Ability to tell stories with data and knowledge of complex visualization techniques preferred Proven experience in managing multiple analytic projects concurrently Management of analytical activities of external service providers is highly preferred Strong sense of urgency, ownership and proactive attitude to deliver value to our brands Ability to adapt and communicate messages to a wide range of audiences at all levels (both scientific and commercial), inside and outside of the organization. Able to clearly articulate highly technical methods and results to diverse non-technical audiences to drive decision making Apply growth mindset to develop new skills or knowledge Fluency in spoken and written business English mandatory

Sauge AI Market Intelligence

Industry Trends

The pharmaceutical industry is experiencing unprecedented growth in real-world evidence requirements, driven by regulatory agencies like FDA and EMA increasingly demanding post-market surveillance and comparative effectiveness studies. This trend is creating massive demand for specialized data scientists who can bridge the gap between traditional clinical trial data and real-world patient outcomes, making this role particularly strategic for career advancement. Healthcare digitization is accelerating globally, with electronic health records, claims databases, and patient registries generating petabytes of structured and unstructured data annually. Organizations are investing heavily in advanced analytics capabilities to extract actionable insights from these data sources, creating a competitive advantage in drug development and market access strategies. The integration of artificial intelligence and machine learning in pharmaceutical research is transforming how companies approach drug safety monitoring, patient stratification, and treatment optimization. Companies are specifically seeking professionals who can apply deep learning and NLP techniques to healthcare data, representing a significant evolution from traditional biostatistical approaches. Regulatory frameworks are evolving to accommodate real-world evidence in drug approval processes, with recent FDA guidance documents emphasizing the importance of robust RWE studies for label expansion and post-market commitments. This regulatory shift is driving pharmaceutical companies to build internal capabilities rather than relying solely on external vendors.

Role Significance

Typically operates within a specialized team of 8-12 RWE professionals, collaborating extensively with cross-functional teams including medical affairs, regulatory, market access, and digital analytics. The role involves managing external service providers and coordinating with global matrix teams, suggesting influence over 20-30 professionals across various projects.

This is a senior individual contributor role with significant strategic influence, evidenced by the requirement to advise business leaders and drive methodological innovation. The position involves high-stakes decision-making that directly impacts regulatory submissions and market access strategies, indicating substantial organizational trust and responsibility.

Key Projects

Leading multi-year post-market surveillance studies for newly approved diabetes and cardiovascular therapies, involving analysis of millions of patient records across multiple countries to demonstrate real-world safety and effectiveness profiles Developing AI-powered predictive models for patient outcome optimization in transplant medicine, utilizing machine learning algorithms to identify optimal treatment protocols and reduce rejection rates Creating comprehensive health economic models that demonstrate cost-effectiveness of innovative therapies to support payer negotiations and market access decisions in key European markets Implementing federated learning approaches to analyze sensitive patient data across multiple healthcare systems while maintaining privacy compliance, enabling unprecedented scale in real-world evidence generation

Success Factors

Mastery of advanced statistical methods combined with practical understanding of healthcare data complexities is essential, as the role requires translating sophisticated analytical techniques into reliable evidence that can withstand regulatory scrutiny and peer review processes. Strong stakeholder management and communication skills are critical for success, as the role involves presenting complex analytical findings to diverse audiences including physicians, regulators, payers, and senior executives who may lack technical backgrounds but make decisions based on these insights. Ability to navigate complex global regulatory environments and data privacy requirements while maintaining scientific rigor is fundamental, as RWE studies must comply with varying international standards including GDPR, HIPAA, and emerging AI governance frameworks. Continuous learning mindset and adaptability to emerging technologies is vital, as the field is rapidly evolving with new methodologies, data sources, and regulatory requirements that can fundamentally change analytical approaches within months rather than years.

Market Demand

Very High - The convergence of regulatory requirements, digital health transformation, and AI adoption in pharmaceuticals is creating exceptional demand for professionals with this specific skill combination. The role sits at the intersection of multiple high-growth areas within life sciences.

Important Skills

Critical Skills

Advanced Python programming with healthcare data focus is absolutely essential, as the role requires daily manipulation of complex, multi-source healthcare datasets that often contain millions of patient records with intricate data quality issues requiring sophisticated cleaning and transformation techniques Deep understanding of causal inference methods and propensity scoring is fundamental to the role's success, as real-world evidence studies must demonstrate causality rather than mere correlation to meet regulatory standards and support product labeling claims Expertise in machine learning and deep learning applications to healthcare data is increasingly critical, as the pharmaceutical industry rapidly adopts AI-driven approaches to patient stratification, outcome prediction, and treatment optimization Strong communication skills for translating technical findings to non-technical stakeholders are vital, as the role's impact depends on the ability to influence clinical, regulatory, and commercial decisions through clear presentation of complex analytical results

Beneficial Skills

Experience with federated learning and privacy-preserving analytics techniques is becoming increasingly valuable as healthcare data privacy regulations tighten and organizations seek to analyze sensitive patient data across multiple institutions Knowledge of regulatory submission processes and health technology assessment methodologies provides significant advantage, as RWE studies increasingly support regulatory filings and payer negotiations Expertise in natural language processing and text mining of clinical notes and medical literature is highly beneficial, as unstructured healthcare data represents a largely untapped source of patient insights Understanding of health economics and pharmacoeconomic modeling adds substantial value, as cost-effectiveness demonstration becomes increasingly important for market access in value-based healthcare systems

Unique Aspects

This role offers rare exposure to cutting-edge AI applications in healthcare, including generative AI and federated learning approaches that are just beginning to be implemented in pharmaceutical research, providing significant competitive advantage for career development

The position provides direct influence on regulatory strategy and market access decisions for products affecting millions of patients globally, offering unusual combination of technical depth and strategic business impact

Access to Sanofi's extensive global healthcare data ecosystem, including partnerships with major healthcare systems and payer organizations, provides unparalleled learning opportunities in real-world evidence generation

The role sits at the intersection of multiple high-growth areas including AI in healthcare, personalized medicine, and digital health transformation, creating unique positioning for future career opportunities

Career Growth

Progression to senior management roles typically occurs within 3-5 years for high performers, with opportunities for rapid advancement given the strategic importance of RWE capabilities and the scarcity of professionals with this expertise level.

Potential Next Roles

Senior Director of Real-World Evidence, leading enterprise-wide RWE strategy and managing teams of 15-20 data scientists across multiple therapeutic areas Head of Digital Health Analytics, overseeing the integration of AI and digital health technologies into pharmaceutical research and development processes Chief Data Officer roles in mid-sized pharmaceutical companies, responsible for enterprise data strategy and advanced analytics capabilities Regulatory Affairs leadership positions focusing on digital health and real-world evidence submissions to global health authorities Consulting partnership opportunities with major life sciences consulting firms, advising pharmaceutical companies on RWE strategy and implementation

Company Overview

Sanofi

Sanofi is a multinational pharmaceutical corporation headquartered in France, ranking among the top 5 global pharmaceutical companies by revenue with approximately EUR 37 billion in annual sales. The company has a strong presence in immunology, diabetes, and rare diseases, with significant investments in digital health transformation and real-world evidence capabilities as part of its strategic evolution toward precision medicine.

Sanofi maintains a leading position in several therapeutic areas including diabetes care (through its Lantus franchise), immunology (with blockbuster drugs like Dupixent), and vaccines (as one of the world's largest vaccine manufacturers). The company's focus on real-world evidence represents a strategic initiative to maintain competitive advantage in an increasingly complex regulatory and payer environment.

Berlin represents a key European hub for Sanofi's digital health and analytics operations, benefiting from Germany's strong healthcare infrastructure and proximity to major European regulatory bodies. The location provides access to diverse healthcare data sources and collaboration opportunities with leading academic medical centers and research institutions.

Sanofi promotes a collaborative, science-driven culture with strong emphasis on patient impact and innovation. The company's 'Play to Win' practices emphasize entrepreneurial thinking and rapid execution, while maintaining rigorous scientific standards. The hybrid work model reflects modern pharmaceutical industry practices, balancing collaboration needs with flexibility for analytical work.

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Real-World Evidence Data Scientist - Job Opportunity at Sanofi

Benefits

Key Responsibilities

Requirements

Education

Experience

Required Skills

Sauge AI Market Intelligence

Industry Trends

Role Significance

Key Projects

Success Factors

Market Demand

Important Skills

Critical Skills

Beneficial Skills

Unique Aspects

Career Growth

Potential Next Roles

Company Overview

Sanofi

Data Sources & Analysis Information

Job Listings Data

Sauge AI Market Intelligence