Company Description
AbbVie’s mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people’s lives across several key therapeutic areas – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.
Job Description
The Computational Toxicology group is dedicated to advancing in-silico methods that enhance the prediction and understanding of safety and toxicology for both small and large molecules. Team members will work with diverse biology-related datasets, ranging from pharmacology, toxicology, genomics, and chemistry, applying data science and machine learning techniques. The primary goal is to leverage data effectively and identify useful insights. This role focuses on leveraging computational expertise to process and analyze biological datasets for predictive modeling and novel safety-related discoveries.

QUALIFICATIONS

• Bachelor’s Degree with 3 or more years’ of relevant experience; Master’s Degree with 0-2 years’ of relevant experience.
• Background in life sciences or work experience in the pharmaceutical industry preferred.
• Proficiency in bioinformatics and data science tools, with expertise in Python and experience with parallel and/or cloud computing. Familiarity with database management systems and advanced querying techniques for efficiently handling and extracting insights from large datasets.
• Solid understanding of machine learning techniques, including supervised/unsupervised learning, clustering, classification algorithms (e.g., SVMs, random forests, gradient boosting trees, deep learning), and predictive modeling.
• Experience with advanced AI techniques, such as generative models (e.g., GANs, VAEs) and large language models (LLMs), is highly desirable.
• Experience in statistical methods, such as hypothesis testing, Bayesian inference, time-series analysis, and multivariate analysis, particularly applied to biological datasets.
• Experience with data visualization and interface development with an emphasis on biological data representation.
• Ability to multitask and work within timelines.
• Theoretical and practical knowledge to carry out the job functions
• Strong written and oral English communication skills.
  Additional Information
  AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
  US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
  US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
  https://www.abbvie.com/join-us/reasonable-accommodations.htm

• Collaborate with research teams and data scientists to design and implement data-driven strategies, utilizing machine learning/AI methods to support discovery and preclinical safety studies. Work closely with scientists to co-develop tools and solutions tailored to the most relevant and pressing research problems, ensuring that computational approaches align with scientific objectives.
• Design, develop, and implement solutions with applications including, but not limited to, chemistry, in vitro, preclinical, clinical, and genomic datasets.
• Identify, curate, and process internal and external biology and safety-related datasets. Apply data science methodologies to harmonize and analyze complex datasets, uncovering associations that inform safety assessments.
• Develop predictive models, analytical tools, and intuitive user interfaces to translate computational findings into actionable insights, enabling safety risk prediction.
• Communicate results and methods clearly to both scientific and non-technical audiences, ensuring effective knowledge transfer across teams.