Associate Director Data Scientist

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Job Function:

Data Analytics & Computational Sciences

Job Sub Function:

Data Science Portfolio Management

Job Category:

Professional

All Job Posting Locations:

San Diego, California, United States of America

Job Description:

Nonclinical Safety & Submissions (NCSS), Preclinical Sciences and Translational Safety (PSTS), is seeking a highly motivated Associate Director Data Scientist to lead the delivery of transformative data analytics and capabilities that will drive diagnostic efficiency, holistic data interpretations, and robust mechanistic insights to power the new discovery paradigm. NCSS is a team of toxicologists and nonclinical writers responsible for delivering end to end on the PSTS portfolio (efficacy-to-safety, Discovery through Post-Marketing) across all J&J modalities and therapeutic areas.

The Associate Director Data Scientist will advance the digital transformation of NCSS by deploying data and nonclinical submissions solutions across NCSS functions to interrogate, analyze and interpret multimodal nonclinical safety data, enabling the integration and interpretation of study data, meta-analyses, mechanistically founded hypothesis generation and predictive modeling. Success in this role requires strong matrix leadership skills, effective stakeholder management, and a breadth of biology and computational knowledge to translate key concerns and portfolio needs into meaningful data science solutions and investigating those using relevant data analytics and visualization techniques. The candidate is proactive, intellectually curious and will thrive in a dynamic, multidisciplinary and collaborative environment. This role is expected to provide support across all NCSS functions.

Principal Responsibilities:

• Partner with NCSS stakeholders, prioritize and implement NCSS data needs and use cases, including ensuring FAIR NCSS metadata.
• Successfully collaborate and deliver results in a matrixed environment across PSTS functions (including BDDS, TPPIT, SIS, and Pathobiology stakeholders) and DPDS (IT and Data Science) to assess data landscape and develop solutions in line with strategy, implementation roadmaps, and action plans.
• Independently develop solutions for NCSS data search, interpretation, reporting and communication to stakeholders; deploy relevant data transformations, visualizations, ​summaries and analytics; deploy quality control measures to ensure accuracy and completeness of analyses and solutions; develop and implement statistical and machine learning models applied to multimodal toxicology data.
• Serve as a subject matter expert in Nonclinical Safety & Submissions data science, capable of recommending the optimal solution and leading development and implementation. Provide expertise and mentorship on NCSS data science methodologies/exponents. Provide scientific leadership in data analytics, fostering a data-driven culture, and applying innovative approaches.
• Interpret and communicate outcomes of analyses with independence in report writing and PowerPoint presentations. Collaborate, present, and communicate project-related deliverables and findings in multidisciplinary PSTS meetings.

Education:

• A minimum of a Master’s degree in Biology or a Computational Science (Computer Science, Biostatistics, Engineering, or Data Science or related field) with experience in programming and data science is required
• A PhD in Biology or a Computational Science, (Computer Science, Biostatistics, Engineering, or Data Science or related field) with experience in programming and data science is highly preferred

Required

• A minimum of 8 years of Data Science experience in an industry or academic setting post-degree in positions of increasing responsibility is required
• Good knowledge of SAS required
• Proficiency in programming (e.g. Python, R, SQL, Javascript) and experience with Cloud Platforms (e.g. GCP, Azure, AWS) is required
• Skill in data visualization and dashboarding tools (e.g. Spotfire, PowerBI, R-Shiny) is required
• Proficiency in the use of statistical models and AI (e.g. multivariate linear regression, cluster algorithms, decision trees, logistic regression, Principa