supervisor | Core Engineering - Data Intelligence - Research Engineer in New York, NY

Core Engineering - Data Intelligence - Research Engineer

  • Goldman Sachs USA
  • $107,230.00 - 200,310.00 / Year *
  • 172 W 104th St
  • New York, NY 10025
  • Full-Time



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. A research scientist in this team is responsible for deeply understanding one or more problem spaces, identifying high impact business opportunities, clearly formulating them as machine learning tasks, deftly designing and developing performant, scalable, and resilient algorithms and solutions, readily deploying the solutions, monitoring performance to ensure the desired business impact, and effectively communicating broadly at the firm. Research scientists have a PhD or equivalent in machine learning or a closely related area. A research engineer has similar responsibilities, with a greater emphasis on software engineering and a lesser emphasis on independent algorithm and solution design. Research engineers have an MS or equivalent in machine learning or computer science or a closely related area. RESPONSIBILITIES AND QUALIFICATIONS HOW YOU WILL FULFILL YOUR POTENTIAL The Goldman Sachs central machine learning team uses predictive modeling, natural language processing, anomaly detection, time series forecasting, deep learning, and other techniques on high-volume and high-velocity data to solve problems of significant business value across multiple divisions of the firm. SKILLS AND EXPERIENCE WE ARE LOOKING FOR 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.
Core Engineering - Data Intelligence - Research Engineer

Associated topics: circuit, c c++, electronic, informatic, java, malware, photonics, radar, robotics, software


* The salary listed in the header is an estimate based on salary data for similar jobs in the same area. Salary or compensation data found in the job description is accurate.

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