Real-World Evidence Data Scientist

Real-World Evidence Data Scientist

Requirements

• Master’s degree in quantitative fields such as statistics, applied mathematics, computer science, or related field. PhD is preferred
• Expert knowledge in RWE, pharmaco-epidemiology, health outcomes research, statistical methods, etc.
• Experience in R, Python or data base programming (SQL) is a must
• 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
• Fluency in spoken and written business English mandatory

Responsibilities

• Provide technical expertise for the design and delivery of studies using real world data; ensure scientifically rigorous methods are applied for addressing medical, economic and outcomes research questions
• Effectively translate business requirements into data analysis specifications
• Verify data source integrity and validity to ensure compliance to data and ethical standards deployed throughout the data transformation process
• Apply data science expertise in machine learning, deep learning, artificial intelligence, text-mining/NLP, predictive modeling and optimization to multiple analytics projects to transform data into meaningful and actionable insights and evidence
• Collaborate with other internal or external experts and departments as needed to secure the completion of analyses to the highest scientific rigor and standards
• Provide clear explanations on analysis outputs to support appropriate interpretation of study results and/or insights that drive business decisions
• Promote best practices and adhere to standards for data science processes, including documentation and code developments
• Be a strong internal expert in data science while staying ahead of new methodological developments in the RWE Data Science field. Be an expert in advanced analytics and advise business leaders in machine learning, deep learning, text-mining/NLP, etc
• Maintain up-to-date with industry practices and emerging technologies such as generative AI and test creative ways of offering AI solutions to enhance existing processes and solutions
• Maintain timely communication and close alignment with team members, partners and stakeholders to ensure clear and timely visibility to the project progress and anticipation of challenges and risks
• Accountable for supporting GenMed medical gaps within the Global Integrated Evidence Generation Plans (IEGP)

Preferred Qualifications

• 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
• 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