Data Scientist - Machine Learning Engineer
Posted: October 14, 2017
Reference ID: 112466BR
We are in a data science renaissance.
Companies that embrace data science will lead and those who do not will fall behind.
To help IBM's clients lead, we are building an elite team of data science practitioners to help them learn how to succeed with data science. The team will include data engineers, machine learning engineers, operations research / optimization engineers and data journalists.
The team will engage directly in solving real-world data science problems in a wide array of industries around the globe with IBM clients and internally to IBM. The elite team of data scientist will work with other IBMers and client data science teams to solve problems in banking, insurance, health care, manufacturing, oil & gas and automotive industries, to name a few. We will teach the data scientists and sometimes people who desire to be data scientist to:
1. Identify a use case
2. Break that use case down into discrete MVPs (minimal viable product)
3. Work in code notebooks
4. Build & validate models
5. Deploy models via APIs into applications or workflows
6. Monitor & retrain models
7. Use code repositories to version and share code/notebooks
8. Visualize the output of their data story in a way that is consumable by all
9. Create Machine Learning pipelines and train models.
10. Build visual scenario comparison prototypes for \"before\" vs \"after\" KPI comparison, specifically for optimization-based decision-making applications
11. Communicate effectively with line-of-business end-users to discover pain points and use cases, lead project definitions, and convey the
business value of the project
Preferred Work Locations: New York and San Francisco
While working across all these industries, you will also get to travel the World as these engagements will require that the team spend several weeks at client sites working on data science problems with a diverse team.
As a member of the team you will have a T-shaped skill set, having a broad knowledge base in Data Science and Industry Solutions in general, but also in- depth expertise in Operations Research / Decision Optimization.