Company:
AbbVie
Location: Mettawa
Closing Date: 24/10/2024
Hours: Full Time
Type: Permanent
Job Requirements / Description
Job Description
We are currently seeking a highly skilled Data Scientist to lead a talented team responsible for analyzing large amounts of raw information to find patterns that will help improve the data to insights journey for our US Commercial Organization. In this pivotal role, you will be responsible for training and developing Abbvie’s commercial data scientists to build sophisticated data products that extract valuable business insights and drive efficient and effective communication with doctors and patients. This role demands exceptional analytical capabilities, a strong background in mathematics and statistics, and the ability to think critically and solve complex problems as well as simplify complex communications for a wide variety of senior leaders. A genuine passion for machine learning and a commitment to continuous research are also essential. Join us on this professional journey and make a profound impact in Abbvie’s mission to improve patient’s lives.
What you’ll be doing:
The Associate Director of Commercial Data Science will be responsible for leading a team that’s designing, executing and socializing analytical solutions to business problems across Abbvie. You will guide data scientists to apply statistics, machine learning, and operations research techniques to enhance efforts in generating revenue, driving efficiencies, identifying cost savings, and developing products. You will work closely with business stakeholders and technical partners across the organization 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 and a strong desire to build talent, design successful organizational structures, implement best practices, and drive the successful application of machine learning in the commercial space. The ability to work in a diverse team and the willingness to learn are key to this role, as is the ability and desire to communicate wins and opportunities for leadership.
Core Job Responsibilities:
Lead a team of 4 or more data scientists, including recruitment, onboarding, training, and development.
Ensure data scientists within the team have the skills, training, and tools to construct and develop predictive models that are reliable, scalable, and modular.
Consult with stakeholders to define business questions, requirements, timelines, objectives, and success criteria that enable durable utilization of machine learning
Report, visualize and communicate results to business stakeholder up to and including GMVPs on a regular basis.
Approve and enhance existing machine learning models built by the core team.
Develop a plan to put machine learning models into production at scale.
Adopt new tools/techniques to increase performance, automation, and scalability. Design and define approach to scale and operationalize models for machine learning.
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)
Team will 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
We are currently seeking a highly skilled Data Scientist to lead a talented team responsible for analyzing large amounts of raw information to find patterns that will help improve the data to insights journey for our US Commercial Organization. In this pivotal role, you will be responsible for training and developing Abbvie’s commercial data scientists to build sophisticated data products that extract valuable business insights and drive efficient and effective communication with doctors and patients. This role demands exceptional analytical capabilities, a strong background in mathematics and statistics, and the ability to think critically and solve complex problems as well as simplify complex communications for a wide variety of senior leaders. A genuine passion for machine learning and a commitment to continuous research are also essential. Join us on this professional journey and make a profound impact in Abbvie’s mission to improve patient’s lives.
What you’ll be doing:
The Associate Director of Commercial Data Science will be responsible for leading a team that’s designing, executing and socializing analytical solutions to business problems across Abbvie. You will guide data scientists to apply statistics, machine learning, and operations research techniques to enhance efforts in generating revenue, driving efficiencies, identifying cost savings, and developing products. You will work closely with business stakeholders and technical partners across the organization 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 and a strong desire to build talent, design successful organizational structures, implement best practices, and drive the successful application of machine learning in the commercial space. The ability to work in a diverse team and the willingness to learn are key to this role, as is the ability and desire to communicate wins and opportunities for leadership.
Core Job Responsibilities:
Lead a team of 4 or more data scientists, including recruitment, onboarding, training, and development.
Ensure data scientists within the team have the skills, training, and tools to construct and develop predictive models that are reliable, scalable, and modular.
Consult with stakeholders to define business questions, requirements, timelines, objectives, and success criteria that enable durable utilization of machine learning
Report, visualize and communicate results to business stakeholder up to and including GMVPs on a regular basis.
Approve and enhance existing machine learning models built by the core team.
Develop a plan to put machine learning models into production at scale.
Adopt new tools/techniques to increase performance, automation, and scalability. Design and define approach to scale and operationalize models for machine learning.
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)
Team will 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
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