Job Description

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  . 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.

Responsibilities: 

• 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.

Qualifications

• Bachelor’s Degree with 4 or more years’ of relevant experience; Master’s Degree with 2 or more years’ of relevant experience.
• 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

Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law: ​​

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• The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.​​

• We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.​​

• This job is eligible to participate in our short-term incentive programs. ​​

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Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law. 

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. 

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