Associate Data Scientist (all genders) - fix term position

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

We are looking for a curious, creative, and business oriented Data Scientist, to work within our Global Winning with Data team to provide technical expertise in Advanced/Predictive/Prescriptive Analytics. Key responsibility will be to help execute Data Science projects for the highly visible and groundbreaking Digital Transformation and Winning with Data initiatives. For this role, you should be highly analytical with a love and high affinity for analysis, math, and statistics. A strong candidate for this position should not only be skillful in quantitative methodologies and programming (holding themselves to a high standard for coding best practices), but also have keen business acumen. One should have a passion for analysis, data exploration, machine-learning, and out of the box thinking. Utilization of related technical skills and methodologies and strive to remain abreast of leading-edge data science technologies.

What you'll be doing:

• Serve as a key team member on the Advanced Data Science arm of the Global Winning with Data team, working in close partnership with the Digital Transformation and Wining with Data Global Commercial Capability teams and becoming a trusted partner to Global, Area and Affiliate leadership, ensuring data and digital strategy and key deliverables reflect advanced data science best practices
   
• Develop innovative predictive models that are reliable, scalable, and modular and drive to business relevant outcomes
   
• Maintain, optimize, scale, and operationalize existing machine learning models in concert with key partners such as the Digital Transformation team, BTS, and external vendors/partners
   
• Utilize data sets of varying degrees of size and complexity and develop novel way to create creative predictive analytic solutions to work around data limitations
   
• Proactively identify new data science approaches that would improve our ability to answer commercial business questions by closely collaborate with key stakeholders to transform business questions to algorithms and further support the business needs
   
• Work with stakeholders to define business questions, requirements, timelines, objectives, and success criteria for data science initiatives
   
• Work closely with Business Technology Solutions (BTS) colleagues to ensure smooth transition of pilots into production, including technical guidance with respect to replication and scaling
   
• Provide data science thought leadership in the Digital Transformation and Winning with data Capabilities strategic planning processes based on a deep understanding of advanced data science best practice approaches and their application to AbbVie’s commercial data to address business needs
   
• Ensure Advanced Data Science components and projects he/she is involved with for Digital Transformation and/or Winning with Data activities are conducted on time, accurately, within budget, and in accordance with financial and ethical compliance guidelines

Qualifications

• BS degree required, with demonstration and focus in applied data science, in Statistics, Engineering, Operations Research, Computer Science or Economics
   
• Desirable: Advanced degree (Masters, PhD) in Statistics, Math, or Computing Science
   
• Portfolio of work demonstrating data science applications (e.g. via GitHub/publications), including some or all of the following languages: Pyspark (highly desirable) and Python along with data science applications (e.g. via GitHub/publications)
   
• Desirable: Expertise in other languages such as R, Java, C (C++, C#)
   
• Desirable: Knowledge and usage of recommendation, clustering, segmentation algorithms, mix modeling, forecasting, optimization
   
• Minimum of 4 years of applied data science and fields such as data engineering and predictive modeling or strong academic background in Statistics, Mathematics, Physics, Computer Science or Computer Engineering
   
• Experience developing and executing data science to address key business questions with positive, measurable outcomes
   
• Experience analyzing and drawing insights from key internally generated and external data sets
   
• Analytical reasoning abilities, intellectual curiosity, strong business acumen, and creativity in
  problem solving
   
• Ability to interact effectively with/present to (verbally and in writing) all levels of the organization
   
• Ability to be agile & nimble to changing business conditions, strong collaboration, and teamwork
  across functional groups to contribute to a productive work culture. Lead strategically and create
  trust and respect within the department.

Additional Information

AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives, serving our community and embracing diversity and inclusion.  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 protected veteran, or any other legally protected group status.

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.html