Engineering - Platform - Enterprise Technology Operations - Machine Learning Developer - Associate - Dallas

  • Competitive
  • Dallas, TX, USA Dallas TX US
  • Permanent, Full time
  • Goldman Sachs USA
  • 22 Mar 18 2018-03-22

Engineering - Platform - Enterprise Technology Operations - Machine Learning Developer - Associate - Dallas


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.

Design, engineer, deploy & lifecycle manage the firm's voice, multimedia systems, messaging platforms, desktop and productivity apps including; MS Exchange, perimeter gateways, MS Office, Outlook, Visio, Google docs, Trader voice platforms, call manager and Telepresence. The group ensures the resiliency, optimization, security and protection of our systems and the delivery and management of the firm's robust product groups.

ETO's Machine Learning team uses statistical modelling, anomaly detection, predictive modelling, time series forecasting and other techniques on big and fast data to reduce the risk and cost of managing the firm's massive compute infrastructure and applications. An individual in this role is responsible for deeply understanding our problem space, swiftly identifying high impact business problems, succinctly formulating them as statistical modelling or machine learning tasks, deftly developing performant, scalable, and resilient production-grade models, readily deploying those models to production, constantly monitoring model performance to ensure delivery of the desired business impact, and effectively communicating the impact broadly at the firm.


Basic Qualifications
• A Bachelor's degree (Masters/ PhD preferred) in a computational field (Computer Science, Applied Mathematics, Engineering, or in a related quantitative discipline), with 3+ years of experience as an applied data scientist (or equivalent).]
• Understanding of applied statistics and fundamental ML principles and techniques
• Ability to apply fundamental algorithms and data structures to efficiently solve computational problems
• Working knowledge of more than one programming language (Python, R, Java, C++ etc.)
• Ability to stay commercially focused and to always push for quantifiable commercial impact
• Strong work ethic, a sense of ownership and urgency
• Strong analytical and problem solving skills
• Ability to collaborate effectively across global teams and communicate complex ideas in a simple manner

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.