Data Science Machine Learning Engineer/ Strategist, Securities, Analyst/Associate, London

Job description

Job Summary & Responsibilities

Have a global impact with Goldman Sachs without moving!

We are hiring people who want to work on interesting challenges with a global impact in the financial sector.  We have 1.5BN lines of code running across a huge infrastructure. We also give back to the Open Source community (e.g. Eclipse collections) and stay close to the start-up scene. Our people have complete product ownership; requirements, coding, testing and deployment.  We run a low friction SDLC where you learn from peer code review. You will help us progress toward our goal of continuous build and cloud deployment everywhere.

We like to run a lean and flat structure where everyone is happy to help out. We love to learn and offer robust training resources, including free access to Pluralsight, Safari books, Harvard Business Review and Egghead training platforms. We run internal MOOC-like curated learning, soft skills education, a women's mentoring program and more. We have a culture of giving back; last year over 25,000 of our people volunteered on 1,600 community projects across the world.

Job Summary

Machine learning problems we aim to tackle are among the most challenging ones due to competitive nature of the markets. The front-office machine learning strats team continuously challenges the status-quo of the conventional approaches in financial services.
We are seeking bright individuals to join us in solving hard data science and machine learning problems on large scale complex financial data sets. We are looking for candidates who have:

• Quantitative rigour - data drives everything we do
• Passion to deliver - our job is not done until the system works and makes bottom-line impact
• Creative drive - we face unique problems daily that require applying unique quantitative approaches
• Great communication skills - as daily coordination with our global teams is essential

Responsibilities

• Work as part of the front-office machine learning strats team. Deliver components of data flow and machine learning models from conception to production. Share ownership of end-to-end development and deployment of the front-office system
• Work with large and complex financial data sets unique to our business. Provide insights to data, build visualisations based on quantitative methods
• Interact closely with local and global engineering and other front-office teams
• Work with various technologies including R, Python, Java, C++, Linux, SQL and Goldman Sachs’ proprietary technologies: Slang and SecDb.

Goldman Sachs is an equal employment/affirmative action employer Female/Minority/Disability/Vet.  © The Goldman Sachs Group, Inc., 2017.  All rights reserved.

Basic Qualifications

• BSc or MSc degree in a quantitative discipline
• Solid programming skills in one of Java, Python, C++ with background in algorithms data structures
• Full software development life-cycle experience
• Experience with database languages (e.g. SQL, KDB)

Preferred Qualifications

• MSc or PhD degree in a quantitative discipline
• Expertise in statistical analysis, stochastic models, multi-variate analysis and / or related fields
• Experience in statistical tools / languages (e.g. R, pandas / numpy / scikit, Matlab)
• Experience in building big data / machine learning pipelines
• Experience in TensorFlow or similar deep learning frameworks (Theano, Torch, Keras)
• Exposure/background in financial products, front-office background is a plus