The Computational Toxicology group is dedicated to advancing insilico methods that enhance the prediction and understanding of safety and toxicology for both small and large molecules. Team members will work with diverse biologyrelated 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 safetyrelated discoveries.
Responsibilities:
- Collaborate with research teams and data scientists to design and implement datadriven strategies utilizing machine learning/AI methods to support discovery and preclinical safety studies. Work closely with scientists to codevelop 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 safetyrelated 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 nontechnical audiences ensuring effective knowledge transfer across teams.
Qualifications :
- Bachelors Degree with 3 or more years of relevant experience; Masters Degree with 02 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 timeseries 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 & Puerto Rico applicants seeking a reasonable accommodation click here to learn more:
Work :
No
Employment Type :
Fulltime