Data Science Machine Learning Engineer / Strat – VP Level

  • Competitive Market Rate
  • London, England, United Kingdom
  • Permanent, Full time
  • Anson McCade
  • 14 Dec 17 2017-12-14

The front-office machine learning strats team continuously challenges the status-quo of the conventional approaches in financial services. They are seeking bright individuals to join them in solving hard data science and machine learning problems on large scale complex financial data sets.

Data Science Machine Learning Engineer / Strat – VP Level

 

London based

 

The front-office machine learning strats team continuously challenges the status-quo of the conventional approaches in financial services. They are seeking bright individuals to join them in solving hard data science and machine learning problems on large scale complex financial data sets.

 

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.

My client are looking for candidates who have:

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

Requirements

  • MSc or PhD 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).
  • 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.