Summary:
The Computational Toxicology group functions to develop, refine, and bring awareness to in-silico methods that may help predict and understand safety and toxicology for small and large molecules.  Team members will gain exposure to a wide range of safety-related datasets ranging from biology, pharmacology, toxicology, and chemistry to help with predictive modeling and data analysis.  Using these datasets, the team hopes to apply data science and machine-learning methodology to better visualize the data and help identify novel and mechanistic associations.

Responsibilities:

• Collaborate with research and data scientists to conceive informatics analysis strategies through machine learning/AI algorithms to support discovery and pre-clinical safety studies.  Specific applications can be but not limited to transcriptomics, pharmacology, in vitro/vivo safety data and molecular data integration, etc.
• Identify and process relevant internal and external safety datasets and knowledge resources. Propose and execute computational research to identify associations across safety features by leveraging and harmonizing datasets using machine learning toolkits.
• Develop predictive models, tools and user-interfaces that incorporate learnings from computational work to assist team members to evaluate and predict safety outcomes
• Communicate results and methods verbally and in writing for scientific and non-technical audiences

Qualifications (Position may be filled at various levels depending on experience):

• Bachelor’s Degree or equivalent education (typically 10 years of experience), Master’s Degree (typically 8
  years of experience), or PhD (no additional experience) with experience in Statistics, Computer Science, or a related quantitative field.  Background in life sciences or work experience in the pharmaceutical industry preferred.
• Experience with data science and a variety of coding languages and packages, such as R, Python etc. Expected proficiency in at least Python and R.
• Knowledge of data mining, cleaning and transformation techniques including dimensional reduction, standardization, imputation, and aggregation.
• Familiarity with modern relational databases (SQL, Oracle etc.), BI tools (Power BI, Qlik Sense) and IDEs like Jupyter.
• Experience using visualization techniques for presenting data and analysis as dashboards in tools such as R/Shiny, SpotFire, PowerBI, and Custom web-based solutions.
• Experience in Bioinformatics and next-gen transcriptomic analyses (e.g., Single-cell and Spatial transcriptomics) is a plus. 

AbbVie is an equal opportunity employer including disability/vets. It is AbbVie’s policy to employ qualified persons of the greatest ability without discrimination against any employee or applicant for employment because of race, color, religion, national origin, age, sex (including pregnancy), physical or mental disability, medical condition, genetic information, gender identity or expression, sexual orientation, marital status, status as a disabled veteran, recently separated veteran, Armed Forces service medal veteran or active duty wartime or campaign badge veteran or a person’s relationship or association with a protected veteran, including spouses and other family members, or any other protected group status. We will take affirmative action to employ and advance in employment qualified minorities, women, individuals with a disability, disabled veterans, recently separated veterans, Armed Forces service medal veterans or active-duty wartime or campaign badge veterans. The Affirmative Action Plan is available for viewing in the Human Resources office during regular business hours.