Machine Learning / Data Scientist, Capital Markets Machine Learning / Data Scientist, Capital Markets …

GreySpark Partners
in London, United Kingdom
Permanent, Full time
Last application, 02 Mar 21
GreySpark Partners
in London, United Kingdom
Permanent, Full time
Last application, 02 Mar 21
Posted by:
Helen Dawit • Talent Director
Posted by:
Helen Dawit
Talent Director
As part of GreySpark’s Artificial Intelligence Practice you will be part of a specialised team of data professionals and machine learning specialists focused on a broad range of data science and machine learning projects initiatives. The AI Practice assists capital markets clients to address key data and machine learning challenges. These can range from electronic trading driven data analysis projects to data modelling for specific ad-hoc client projects. GreySpark promotes industry best practices and adds value by working collaboratively with our clients to provide deliverables bespoke to their needs.

To excel at this role, you should be detail-oriented, objective and be able to articulate technical details succinctly and clearly. We are looking for demonstrable experience of working on a wide variety of data science and machine learning projects in a commercial or academic environment, knowledge of data best practices and a keen interest of staying abreast of the latest technological developments that are relevant.

What we offer

At GreySpark Partners, you are empowered to take ownership of your work and intellectual development and to build a network that will further your career ambitions.  We offer a clearly defined career path and apply a meritocratic process in promoting the next tranche of leaders in our growing firm. You will have the opportunity to work in a team-focused, dynamic business that recognises success.


  • Statistical analysis of financial data (client/product/trade datasets)
  • Working with business analysts to define and gather data requirements
  • Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
  • Supervising the data acquisition process if more data is needed
  • Defining validation strategies
  • Defining data augmentation pipelines
  • Analysing the errors of the model and designing strategies to overcome them
  • Deploying models to production

Skills & Qualities

  • Proficiency with machine learning APIs and computational packages (examples: TensorFlow, LightGBM, PyTorch, Keras, Scikit-Learn, NumPy, SciPy, Pandas, H2O, SHAP, Catboost)
  • Experience with big-data technologies such as Hadoop, Spark, SparkML, etc
  • Ability to understand various data structures and common methods in data transformation
  • Excellent pattern recognition and predictive modelling skills
  • Experience with large imbalanced datasets
  • You have proven capability to interact with clients and deliver results, taking ideas to production

Technical skills (optional):

  • Experience with data visualisation tools Qlikview, Tableau, matplotlib
  • You have experience working in DevOps cultures. You are comfortable working with CI/CD tools (ideally IBM UrbanCode Deploy, TeamCity or Jenkins), monitoring tools and log aggregation tools.
  • Ideally, you would have worked with VMs and/or Docker.
  • Experience with Agile technologies (JIRA) and version control software (Git / Subversion)


  • University degree in computer science, mathematics, engineering or other quantitative subjects
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