Senior Manager, Data Science responsible for advanced analytics methodologies at Numeris. Leading a team to ensure accuracy and reliability of data products and processes.
About the role
- Senior Director leading Data Science Innovation at GSK, pioneering solutions in real-world evidence generation for drug development lifecycle. Collaborating with experts to drive efficiency and compliance in analytics.
Responsibilities
- Align RWDMA Data Science initiatives with RWD organizational drug development goals, regulatory requirements (e.g., FDA, EMA), and payer expectations, ensuring strategic impact and compliance, particularly in RWD analytics.
- Lead RWDMA Data Science through a matrix organization, collaborating with biostatisticians, clinical and other subject matter experts, and regulatory specialists to lead innovative applications of Data Science in RWE generation and embed Data Science into RWD workflows to improve efficiency of data processing and analysis.
- Design customized Data Science models tailored to specific RWD analytic applications, including Comparative Effectiveness, Precision Medicine, Predictive Modelling, and Evidence Synthesis.
- Automate coding, including clinical coding and patient identification, and quality control (QC) processes using AI-driven anomaly detection and pattern recognition.
- Develop Natural Language Processing (NLP) tools to automate the creation, review, and validation of analytic plans and protocols.
- Mentor team members in advanced Data Science methodologies, fostering a culture of innovation and technical excellence across real world biostatistics, digital measurement, and other focus areas.
- Develop and manage an external engagement strategy with academic partners and KOLs to foster collaborative research and development in RWD Data Science.
Requirements
- PhD in Data Science, Biostatistics, Computer Science, or a related field.
- 15+ years in healthcare and life sciences, with significant exposure to pharmaceutical and/or medical device industries.
- 10+ years in clinical development or RWE generation within regulated environments, including hands-on leadership of Data Science projects.
- Demonstrated success in deploying DataOps, ModelOps, or MLOps pipelines in cloud platforms (e.g., Azure, AWS).
- Expertise in statistical modelling, AI and machine learning techniques (e.g., Convolutional Neural Networks [CNNs], Recurrent Neural Networks [RNNs], Transformers).
- Proficiency in generative AI (e.g., LLMs, RAG, GANs, VAEs, and diffusion models) and the technical stack and tools (e.g., LangChain, LlamaIndex, CrewAI).
- Strong programming skills in Python, R, TensorFlow, PyTorch, and experience with cloud tools (e.g., Azure ML, AWS SageMaker), containerization (Docker), and version control (GitHub).
- Familiarity with multi-domain real-world data (e.g., clinical records, imaging, genomics, wearables, unstructured text).
Benefits
- health care and other insurance benefits (for employee and family)
- retirement benefits
- paid holidays
- vacation
- paid caregiver/parental and medical leave
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