CCBD Technology - Machine Learning Data Engineer

  • Competitive
  • New York City, NY, USA
  • Festanstellung, Vollzeit
  • Goldman Sachs USA
  • 22 Mär 19

CCBD Technology - Machine Learning Data Engineer

MORE ABOUT THIS JOB What We Do
At Goldman Sachs, our Engineers don't just make things - we make things possible. Change the world by connecting people and capital with ideas. Solve the most challenging and pressing engineering problems for our clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity at the speed of markets.

Engineering, which is comprised of our Technology Division and global strategists groups, is at the critical center of our business, and our dynamic environment requires innovative strategic thinking and immediate, real solutions. Want to push the limit of digital possibilities? Start here.

Who We Look For
Goldman Sachs Engineers are innovators and problem-solvers, building solutions in risk management, big data, mobile and more. We look for creative collaborators who evolve, adapt to change and thrive in a fast-paced global environment.
Consumer and Commercial Banking (CCBD) Consumer and Commercial Banking brings innovative solutions to traditional banking activities. We are a global team of lenders, investors, risk managers, skilled marketers, web experts and banking specialists. We provide a suite of solutions to help our customers meet their personal financial goals. We make direct investments in, and risk manage, a portfolio of corporate loans and securities. And we help transform distressed communities through investments and loans of private capital. Digital Finance Digital Finance, a business unit within CCBD, is comprised of the firm's digitally-led consumer businesses, which include the Marcus deposits and lending businesses, as well as the personal financial management app, Clarity Money. Digital Finance combines the strength and heritage of a 148-year-old financial institution with the agility and entrepreneurial spirit of a tech start-up. Through the use of machine learning and intuitive design, we provide customers with powerful tools that are grounded in value, transparency and simplicity to help them make smarter decisions about their money.

RESPONSIBILITIES AND QUALIFICATIONS WHAT YOU WILL DO
• Integrate fully into the software development lifecycle of the Marcus.com platform. This includes designing distributed systems, writing production code, conducting code reviews and working alongside our DevOps team
• Implement highly scalable classifiers and tools leveraging machine learning, including but not limited to natural language processing, text understanding, classification, pattern recognition, recommendation systems, targeting systems, ranking systems, etc.
• Assessing ML-related vendor products and attending vendor conferences.
• Turn research ML papers into working code by prototyping new approaches and productionizing solutions at scale.
• Collaborate with design, data science, product management, and engineering to build new product features that advance our mission of helping people achieve financial well-being.
• Perform inference on knowledge graphs.
• Effectively communicate insights from complex "black-box" models.

PREFERRED QUALIFICATIONS
Advanced degree (Master, PhD) in Computer Science, Engineering, Mathematics, or related field (or the equivalent combination of education and experience).
2+ years experience in applying, implementing, and/or developing algorithms for machine learning or statistics.
Knowledgeable of core CS concepts such as: common data structures and algorithms, profiling/optimization.
Machine learning domain knowledge-bias-variance tradeoff, exploration/exploitation-and understanding of various model families, including neural net, decision trees, bayesian models, instance-based learning, association learning, and deep learning algorithms.
Deep knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc.

ABOUT GOLDMAN SACHS The Goldman Sachs Group, Inc. is a leading global investment banking, securities and investment management firm that provides a wide range of financial services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. Founded in 1869, the firm is headquartered in New York and maintains offices in all major financial centers around the world.

© The Goldman Sachs Group, Inc., 2018. All rights reserved Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Vet.