Data Scientist and Quantitative Dev Lead

  • Liquidnet
  • London, UK
  • 24/12/2018
Full time Data Science Data Engineering Machine Learning Data Analytics Data Management Statistics Software Engineering

Job Description

With the acquisition of OTAS Technologies in 2017 and our continued global evolution, Liquidnet is expanding its technology and quant team to build disruptive new products to further revolutionize trading and investing. Our products are used by the largest investment firms globally, and we are recognised as an industry-leading innovator in big data analytics, AI and machine learning.

We are a dynamic, technology-driven, and highly productive team of quants, developers and product designers. Deep collaboration with our clients and partners is key, and we benefit from great relationships with the most ground-breaking firms in the asset management industry, who help us to constantly push the limits of technology and analytics.

We have a track record of hiring amazing people and giving them (industry veterans and recent grads alike) autonomy and responsibility from the start. You’ll have the best tools and resources available to get the job done, and thrive in a collaborative environment where our teams are passionate about building great things.

The right candidates will have both a strong mathematical background, and be highly adept technologically.

RESPONSIBILITIES

  • Build, mentor and manage a regional team of data scientists and developers
  • Architect enhanced mathematical models and streaming analytics frameworks and provide hands-on project management.
  • Analyse unique proprietary datasets.    
  • Review and write production code.
  • Partner globally with data scientists, quants, developers, product designers, clients and industry leaders to tackle the important problems faced by the world’s largest asset managers.

SKILLS

  • Bachelors’ degree or higher in Computer Science, Mathematics or related technical focus.
  • 5+ years of relevant experience.
  • Strong mentoring and management skills.
  • A strong foundation in a STEM/numeric discipline.
  • Significant statistical modelling expertise
  • Proficiency in a mainstream programming language, such as Python, C/C++, C#, Java, JavaScript, Haskell or similar.
  • Expertise in software development practices such as version control (git), test frameworks, code review tools, deployment configuration management, continuous integration.
  • Significant experience with machine learning libraries/frameworks (TensorFlow preferred) and methods
  • Excellent communication skills and team orientation