Senior Data Scientist, RBQM Analytics

Purpose

Risk Based Quality Management (RBQM) Analytics is an ambitious team of Data Scientists and Data Engineers with a unique blend of Business, Scientific, and Machine Learning expertise that seeks to unleash the full potential of AbbVie’s data assets by bringing technology and/or data and insights to the forefront of decision making via fit-for-purpose solutions. The Senior Data Scientist role is a key role to enable application of advanced analytics techniques across clinical development continuum. Drives Analytics innovation and experimentation to enable data-driven insights for complex business problems across R&D by deploying advanced analytics techniques like Machine Learning and Visual Analytics.

Responsibilities

• Connects with cross-functional teams to design work product and as an analytics consultant. Anticipates and identifies issues that could affect timelines or quality and develops options and solutions
• Leads and helps establish the design principles and standards to ensure analytical work product is consistent across projects where appropriate and that user experience is optimized
• In addition to standard statistical methods, develops prototypes, test methods and algorithms. Leverages emerging statistical methodologies, ML and AI to drive innovative analytical solutions to create insights
• Enables data-driven insights to support of clinical development continuum, including precision medicine
• Ensures adherence to federal regulations and applicable local regulations, Good Clinical Practices (GCPs), ICH Guidelines, AbbVie Standard Operating Procedures (SOPs), and to functional quality standards. Stays abreast of new and/or evolving local regulations, guidelines and policies related to clinical development
• Identify business needs and support the creation of standard KPIs, reports, and statistical analyses;
• Responsible for coaching and mentoring junior team members.
• Work side by side with cross-functional teams as an analytics consultant to strategize on how analytics can help evaluate the progress of clinical trials, as well as facilitate discussions around emerging risks; Applies machine learning techniques to validate assumptions and predict future behavior