Data Scientist

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Do you enjoy analyzing data? Creating products that solve real-world problems and taking them into production? Working with colleagues to find novel ways to approach difficult problems in fintech?

This position is to work in the FinTech area of Broadcom. Our division provides electronic payments and banking solutions in SaaS using AI/ML to determine risk.

We are looking for people with a strong background in Payment Card fraud detection, and a graduate degree (PhD preferred) in an analytical field, e.g., Physics, Statistics, Electrical Engineering, Computer Science, Applied Mathematics, Data Science.

We are looking for people who:

• Have experience building and deploying fraud-detection machine learning models.
• Have strong scientific coding aptitude – efficient code that is numerically stable and of production quality.
• Are strong analytical thinkers, including reasoning through probabilities and statistics, as well as delving into detailed deterministic thinking and using individual examples
• Have interest in working from start to finish: gather data, clean and prepare, model, code, package, guide into production, and help clients use the results.
• Can communicate complex and sophisticated ideas to people without scientific backgrounds
• Enjoy working in a team environment

?Skills needed include: excellent written and verbal communications, and presentation skills.

Languages needed include a subset of: Python, C++, Unix scripting, SAS, PySpark. A strong command of Unix is preferred.

Experience: PhD in a relevant area and 7+ years experience; prefer those with experience taking machine learning into a production fraud-detection environment. Alternatively, Master's degree plus 10+ years industry experience in a relevant area, including taking machine learning into a production fraud-detection environment.