Job Description
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:

Key Responsibilities:

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. 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
11. Guide and mentor clients to become self-sufficient data science practitioners
12. Guide and mentor clients to become self-sufficient data science practitioners

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.

Required Technical and Professional Expertise

At least 5 years experience – Computer Science, Programming skills
At least 5 years experience – Probability and Statistics
At least 4 years experience – Data Modeling and Evaluation
At least 4 years experience – Big Data and Machine Learning

Preferred Tech and Prof Experience

At least 7 years experience – programming skills in at least two of the following: Python, R, Scala or Java. preference for Python Expert
At least 5 years experience – Ability to consume and deploy data via APIs
At least 4 years experience – in applying supervised, unsupervised and semi-supervised learning techniques
At least 4 years experience – Machine Learning pipeline – data ingestion, feature engineering, modeling including ensemble methods, predicting, explaining, deploying and diagnosing over fitting
At least 5 years experience – in model selection and sampling
At least 2 years experience – deep learning and neural nets
At least 5 years experience – Business and Leadership
Strong leadership experience
Aptitude and interest in Management