Business Intelligence Lead

4 weeks ago

Mumbai, India Abbott Full time
:For years, Abbott’s medical device businesses have offered technologies that are faster, more effective, and less invasive. Whether it’s glucose monitoring systems, innovative therapies for treating heart disease, or products that help people with chronic pain or movement disorders, our medical device technologies are designed to help people live their lives better and healthier.The Global Data Science & Analytics Digital Transformation team leverages machine learning, artificial intelligence, statistical modeling, data mining, and visualization techniques to provide analytics solutions to a wide range of challenging business problems.The

Lead, Business Intelligence

, will collaborate with a small team of data scientists and engineers to deliver data science solutions. They will work on assessing market size, predicting revenue, evaluating unit economics, building models to optimize resource deployment, providing real-time intelligence on market share, and providing insights on future investments. They will be responsible for working on complex business analytics problems in the field of life-saving implantable medical devices and employ machine learning, artificial intelligence, statistical modeling, data mining, and visualization techniques to provide analytics solutions to a wide range of challenging projects. As a key leader on the team, the candidate will curate and analyze large quantities of (sometimes unstructured) data to deliver meaningful and actionable insights to both internal and external stakeholders. The candidate should collaborate effectively with internal stakeholders and cross-functional teams in India and the US. They must present solutions and insights in a concise and effective manner, to technical and non-technical audiences alike.2.

:Education:Master’s degree or higher preferred: in the areas of business analytics, business administration, information technology, computer science/engineering, statistics, math, biomedical engineering, bioinformatics, decision science or similar.Years of experience, both overall and any industry-specific experience:Minimum overall 10 years of working experience.Minimum 8 years working experience in business/digital/analytics/consulting.Analytical skills with a solid foundation in programming (R, SAS, Python, SQL, NoSQL, PostgreSQL) and experience with big-data architecture and tools for big-data analyticsPrior experience in the medical device/pharmaceutical industry or experience working with health systems and/or health insurance claims is a plus.3.Strong oral and written communication skillsAble to work both independently and as a team member to deliver on multiple priority projects.Excellent attention to detail and accuracy.:Major Accountabilities% of TimeAnalyzing available data to deliver business insights that address stakeholder needs 30%Reviewing analysis plans, keeping track of timelines, reviewing output of analyses, providing guidance to the team on next steps 20%Packaging and presenting information in presentations 20%Collaborating with internal and external stakeholders 10 %Developing strategy to address business analytics needs across medical device BUs 10%Understanding and curating data sets available for analysis 5%Team development, coaching, hiring 5%​Follows diverse procedures and problems require consideration of many different approaches. Solutions are selected from alternatives using past experience

​What is an example of a typical problem this job must solve and how it would be solved?Within this role, the Lead must provide actionable data/tools that business users can use to make decisions without complete information. Incomplete data is inherent to the nature of our work, so the individual must use various techniques to try to fill in the gaps to make the data actionable, including modeling and developing logic to triangulate multiple datasets. Ad-hoc requests from the business can be vague and ever-evolving, so the Lead must understand the unique nature of requests to both build data/tools themselves, as well as efficiently coordinate resources at hand, to effectively address business unit needs in an ongoing manner.

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