Job Description

We are looking for a Principal Data Scientist to lead a team that will analyze large amounts of raw information to find patterns that will help improve the data to insights journey for our US Organization. We will rely on your team to build data products to extract valuable business insights. In this role, you should be highly analytical with a knack for analysis, math and statistics. Critical thinking and problem-solving skills are essential for interpreting data. We want to see a passion for machine-learning, research, and developing a team of talented data scientists. Your goal will be to help our enterprise service organizations analyze trends to make better decisions across a portfolio of model assets.

What you’ll be doing:

The Principal Data Scientist of Enterprise Services will be responsible for designing, executing, and socializing analytical solutions to challenges across Abbvie’s enterprise services organizations both as a manager as well as a “hand’s on” contributor. You will apply statistics, machine learning, and operations research techniques to enhance efforts in driving efficiencies, identifying cost savings, and developing products. You will work closely with IT and business stakeholders to develop analytical tools and foster the practice of data science. A candidate for this position should not only be skillful in quantitative methodologies and programming, but also have keen business acumen. Experience with reanalyzing previously existing data sets and tables in order to repurpose them for new use cases. Ability to work in a diverse team and the willingness to learn are key to this role.

Core Job Responsibilities:

Lead a team of 1-3 data scientists dedicated to enterprise services, including recruitment, onboarding, training, and development.

Work with stakeholders to define business questions, requirements, timelines, objectives, and success criteria. 

Develop and implement methods for extracting patterns and correlations from both internal and external data sources using machine learning toolkits.

Construct and develop predictive models that are reliable, scalable, and modular.

Maintain and optimize existing machine learning models.

Develop plan to put machine learning models into production.

Adopt new tools/techniques to increase performance, automation, and scalability.

Report, visualize and communicate results to internal stakeholders on a regular basis.

Actively participate and establish a “test and learn” team environment with a clear prioritized plan that is designed to create a bias for action (fail fast/often).

Examine relevant data and quickly develop an analytics plan that will answer key business questions and create value for clients.

Work with data sets of varying degrees of size and complexity, including both structured and unstructured data.

Transform data into actionable insights and recommendations. Present clear and concise results. This includes processing, cleansing, and verifying the integrity of data used for analysis.

Develop analytical solutions by using and applying appropriate methodology – including, but not limited to, regression, forecasting, clustering, decision trees, simulation, optimization, machine learning, and neural networks.

Design and define approach to scale and operationalize models for machine learning.