Data Scientist

The central Advanced Analytics team at the Abbott Established Pharma Division's (EPD) headquarters in Basel helps define and lead the transformation towards becoming a global, data-driven company with the help of data and advanced technologies (e.g., Machine Learning, Deep Learning, Generative AI, Computer Vision). To us, Advanced Analytics is an important lever to reach our business targets, now and in the future; It helps differentiate ourselves from our competition and ensure sustainable revenue growth at optimal margins. Hence the central AA team is an integral part of the Strategy Management Office at EPD that has a very close link and regular interactions with the EPD Senior Leadership Team. Primary Job Function: With the above requirements in mind, EPD is looking to fill a role of a Data Scientist to build and refine effective Data Science Solutions for Abbott EPD world-wide. Core Job Responsibilities: The Data Scientist rapidly navigates from identifying priorities and helping to generate ideas to implementing solutions. They Participate/drive data collection, cleaning, analysis and interpretation (EDA). Collaborate with the business partner and product owners to ideate on solutions to challenging problems. Generate insightful visualizations to communicate findings. Carry out model selection, validation and possible ways for deployment (in collaboration with the engineering team). Write high quality code with possibility of deployment in mind. Share the learnings and findings with other data scientists contributing to the collaborative environment. Collaborate with Sr. Data Scientists and take full responsibility for analysis and modeling tasks. Build effective and efficient AA solutions to business needs, leveraging available market resources as much as possible. Keep himself/herself committed to continuous learning about the latest trends and technologies. Work closely with the Product Owners and the Engineering team to ensure delivery of the Data Science part of the projects within time, cost and quality. Collaborate with external vendors, evaluating their capabilities and ensuring their alignment with data science standards and project requirements. Continuously engage in hands-on data analysis, modeling, and prototyping DS frameworks to deliver high-quality outputs.