Associate Manager, Data Science
AbbVie’s mission is to discover and deliver innovative medicines 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, eye care, virology, women’s health and gastroenterology, in addition to products and services across its Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on Twitter, Facebook, Instagram, YouTube and LinkedIn.
Title: Associate Manager, Data Science
- To provide insights and analytic solutions to key business questions by leveraging various data science approaches.
The Associate Manager, Data Science will be responsible for executing and socializing analytical solutions to business problems across Abbvie. They will apply statistics, machine learning, and operations research techniques to enhance efforts in generating revenue, identifying cost savings, and developing products. The Data Scientist 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 experience translating analytic results to business insights. The ability to work in a diverse team and the willingness to learn are key to this role.
Core Job Responsibilities:
- Work with stakeholders to define business questions, requirements, timelines, objectives, and success criteria
- 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 systems and approach to operationalize models for machine learning.
- Bachelor's degree required in quantitative fields such as Statistics, Engineering, Operations Research, Computer Science or Economics. Master's preferred. PhD a plus.
- 2-4 years’ progressive business experiences in marketing analytics, database marketing, data engineering and predictive modeling or strong academic background.
- Ability to develop advanced machine learning models using scikit-learn, Keras, or other machine learning frameworks.
- Strong algorithmic design skills. Execute analytical experiments methodically while outputting reproducible research.
- Demonstrated proficiency in Python/R, SQL, Relational databases (Teradata, Oracle etc.), BI tools (PowerBI, Qliksense) and IDEs like Jupyter.
- Experience with big data technologies like Hive, Impala, Hue etc.
- Experience in Pharmaceutical datasets like Patient claims and syndicated data from IQVIA (IMS) and Symphony Health Solutions a big plus.
- Strong problem solving and interpersonal skills and ability to work as part of a diverse team including IT and Line of business analytics teams.
- Bring a strong entrepreneurial spirit and ability to think dynamically.
- Strong communication skills, written and verbal.
- Project management experience; solid attention to detail and operational focus.
Significant Work Activities
Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day)
Job Level Code
Equal Employment Opportunity
At AbbVie, we value bringing together individuals from diverse backgrounds to develop new and innovative solutions for patients. As an equal opportunity employer we do not discriminate on the basis 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, protected veteran status, or any other legally protected characteristic.