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Job Description
Director, Data Scientist
Product Manager, AI-Driven Clinical Trials
The Advanced Analytics team within Clinical Data Sciences is seeking a Director of Data Science to act as product manager for AI-Driven clinical trials to work with clinical development and clinical operations to define, scope, and understand what key AI and analytics capabilities are needed to drive the optimization of clinical trials. In this role, the product manager provides key thought leadership toward how AI/ML can be leveraged to optimize clinical trials operations, improve diversity in trial recruitment, and enhance data-driven decision making in a variety of workstreams within the clinical development space. The product manager will work directly with clinical study teams to understand the technical, user experience, and measurable impact that AI-driven and data-driven decisions can make on the speed, cost, execution, and success of clinical trials.
The data scientist in this role is expected to be a thought leader within the organization in the application of AI/ML toward real-world clinical use cases and is deeply involved in all aspects of technical development of algorithms including coding packages, configuring compute environments, evaluation & review of models, and design of model architecture. The data scientist will interact with various technical roles in the clinical data sciences organization including architects, data engineers, data analysts, and product managers to scope, develop, and operationalize AI-driven applications by providing expertise in model development and operationalization. The data scientist will also interact directly with internal stakeholders in an advisory role.
Responsibilities
- Work with stakeholders in an agile project management framework to plan, design, and execute projects leveraging AI/ML.
- Lead cross-functional teams of business functions, key opinion leaders, research teams, IT organizations, and peer data science organizations to drive the development of data science use cases.
- Coaching and mentoring senior and junior data scientists.
- Provide thought leadership toward the development of strategic capabilities in the AI/ML space for biotechnology.
- Design and implement strategies to create the fundamental technical and business process building blocks that inform how AI/ML and analytics make an impact to clinical operations and clinical development.
- Partner with cross-functional teams to build industry-leading capabilities in operational AI/ML within the way that we execute and run clinical trials.
- Work with key business operations partners in clinical operations to define operating metrics and enable predictive analytics to drive trial design and planning to meet or exceed key operations metrics.
- Plan and execute the full lifecycle of machine learning models from requirements gathering, exploratory data analysis, visualizations, model training, model evaluation, and model deployment and monitoring.
- Act as primary product lead to drive the development of AI use-cases into real-world implementations of AI for clinical operations.
- Identify evidence required to make informed decisions from data toward use cases in R&D. Work with IT partners to gather requisite data.
- Work with IT partners to operationalize datasets required to support model development and analytics pipelines.
- Stay current on the state of the art in machine learning models within the technology and biopharmaceutical industries.
- Independently frame business questions from the perspective of data science and derive data-driven solutions.
- Design and apply advanced modeling solutions in the areas of statistical analysis, bioinformatics, data automation, data mining, machine learning and/or data visualization.
- Understand key aspects of clinical and real-world data that relate to the optimization of clinical trial conduct and execution.
- Recommend data-driven approaches to optimize clinical trials operations.
- Interface with cross-functional stakeholders in clinical operations, clinical study teams, and clinical data sciences to facilitate knowledge sharing and build shared understanding of leveraging advanced analytics to drive business improvement.
Education Requirements
- Minimum Bachelor’s degree with 12 years of experience, Master’s with 10 years of experience, or PhD with 8+ years of experience in data science at biotech or technology companies.
- Degree in engineering or technology areas including but not limited to data science, software engineering, biomedical engineering, chemical engineering, mechanical engineering, or similar.
Technical Skill Requirements
- Operationalization of deep learning algorithms at scale
- Understanding of model evaluation & scoring including avoidance of model bias
- Python or R, SQL
- Understanding of Cloud DevOps on AWS related to data science operations.
- Experience translating ground-up AI/ML research into functional packages to support applications
Project Skill Requirements
- Project management skillsets
- Product management mindset and understanding
- Translating stakeholder needs into technical requirements
- Scoping project requirements and building timelines
- Code management using Git
- Technical documentation
The salary range for this position is: $214,795.00 - $277,970.00. Gilead considers a variety of factors when determining base compensation, including experience, qualifications, and geographic location. These considerations mean actual compensation will vary. This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package. Benefits include company-sponsored medical, dental, vision, and life insurance plans.
For additional benefits information, visit: Gilead Benefits
For jobs in the United States: As an equal opportunity employer, Gilead Sciences Inc. is committed to a diverse workforce. Employment decisions regarding recruitment and selection will be made without discrimination based on race, color, religion, national origin, gender, age, sexual orientation, physical or mental disability, genetic information or characteristic, gender identity and expression, veteran status, or other non-job related characteristics or other prohibited grounds specified in applicable federal, state and local laws.
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