AI/ML Job: Senior Data Scientist - Machine Learning Engineer


IBM

Senior Data Scientist - Machine Learning Engineer at IBM

NYC, New York, United States ๐Ÿ‡บ๐Ÿ‡ธ   (Posted Sep 18 2018)
About the company
At IBM, work is more than a job - it's a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you've never thought possible. To lead in this new era of technology and solve some of the world's most challenging problems.

IBM is a leading cloud platform and cognitive solutions company. Restlessly reinventing since 1911, we are the largest technology and consulting employer in the world, with more than 380,000 employees serving clients in 170 countries. With Watson, the AI platform for business, powered by data, we are building industry-based solutions to real-world problems. For more than seven decades, IBM Research has defined the future of information technology with more than 3,000 researchers in 12 labs located across six continents.

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.

Skills & requirements
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 โ€“ 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

Instructions how to apply
see the website
[ job website ]

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