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Data Science Engineer

G-Research is Europe’s leading quantitative finance research firm. We hire the brightest minds in the world to tackle some of the biggest questions in finance. We pair this expertise with machine learning, big data, and some of the most advanced technology available to predict movements in the financial markets.

The role

You will be joining G-Research’s Technology Innovation Group (TIG), who sit at the forefront of researching industry trends and testing new technologies. TIG works with engineering teams across the business to identify where new developments and products could benefit them, creating prototypes and road-testing solutions in our in-house lab and in the cloud.

This is a great opportunity for an experienced data science engineer who is interested in exploring new software platforms, libraries, and hardware accelerators that could benefit our data science and machine learning teams. You will be working as part of a small, diverse, interdisciplinary, fast-paced team. You will build working prototypes and proof-of-concepts, demonstrating the potential of new technologies to other parts of the business.

You will critically and methodically benchmark new technologies in terms of performance and assess their robustness and potential to add value and integrate into the wide technology landscape at G-Research.

Key responsibilities of the role include:

  • Continuously scouting and evaluating new data science and machine learning-related technologies and approaches
  • Creating and using benchmarking frameworks to performance and stress-test new technologies quickly and effectively against the different kinds of workload we have
  • Supporting functional, interoperability, and usability tests and proof-of-concepts both carried out by TIG directly or in collaboration with other teams in the business
  • Disseminating the results of your testing to the wider business through write-ups or internal blog posts aimed at raising awareness of the ever-changing tech landscape
  • Attending national and international conferences to keep up-to-date on the latest trends and services
  • Using your experience – and lessons learnt while experimenting in the lab – to help define what services, solutions, or approaches TIG should test next

Who are we looking for?

You will have a real passion for innovation as well as a solid and proven track-record in data science engineering, ideally in a role focused on the development of data-science pipelines and tooling. As TIG assesses cutting-edge technologies on a daily basis, you’ll enjoy working in a continuously prototyping environment and learning new skills and technologies on an ongoing basis.

The ideal candidate will also have the following:

  • Strong experience in data science and machine learning frameworks
  • Strong experience using programming languages such as Python or R and libraries, APIs and frameworks such as TensorFlow, Keras, and Spark with and without interactive notebooks
  • Hands-on experience in processing datasets in the low-mid TBs
  • Strong interest in the engineering surrounding data science and hands-on experience in building data processing pipelines including distributed ML and streaming ML
  • Familiarity with Linux, Kubernetes, Containers, Cloud tech, and Databases would be beneficial
  • Experience in moving data science workflows into production
  • A strong background in mathematics to be able to understand the needs of quantitative researchers and be able to assess new technologies and ML frameworks in that context
  • Good communication and interpersonal skills
  • Previous financial experience is not required, although interest in financial modelling and forecasting techniques for time-series data will be beneficial
  • A Masters or PhD degree in a highly quantitative subject (mathematics, statistics, computer science, physics or engineering) is desirable

Why should you apply?

  • Highly competitive compensation plus annual discretionary bonus

  • Informal dress code and excellent work/life balance
  • Comprehensive healthcare and life assurance
  • 25 days holiday
  • 9% company pension contributions
  • Cycle-to-work scheme
  • Subsidised gym membership
  • Monthly company events
  • Central London office close to 5 stations and 6 tube lines

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