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Quantamental Software Engineer

Marshall Wace
Quantamental Software Engineer
London, GB
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Job Description

Marshall Wace is a leading provider of alternative investment solutions with approximately $60 billion of assets under management (“AUM”) and over 400 employees worldwide including London, New York, Singapore and Hong Kong. One of our founding beliefs is that technology and data are at the core of the business allowing us to build and maintain cutting edge hardware and software solutions.

Marshall Wace operates with lean and focused teams and has a culture that encourages interaction across all areas of the business on a global scale. Our aim is to use the best tool for the job therefore provides the opportunity to be constantly learning and utilizing modern technologies.

At Marshall Wace, our teams strive to push boundaries and think innovatively creating an environment that is fast paced, dynamic and successful. Click here to view our first open- source project for a glimpse into our culture, projects and types of technologies we work with. The person in this role will join the Quantamental team and work in close partnership with several stakeholders across Marshall Wace, including Quantamental Research as well as Data Engineering, Quantitative Research and Risk teams.

Key Responsibilities

The ideal candidate for this position will be passionate about data and about building efficient pipelines for data processing as part of a multi- disciplinary team working across multiple projects at any given time. They will have a pragmatic, commercially minded approach to problem solving and will have a demonstrable ability to see the global picture and deliver impactful projects against it. The position will include workflows in technology, data and processes.

QM Tech

  • Collaboratively develop resilient, transparent, and flexible data platform tools for use in QM research
  • Build infrastructure solutions for data pipelines at various scales
  • Design and implement high-performing and robust libraries of re-usable tools for utilization in QM systematic research process (e.g., signals construction, backtesting and diagnostics)
  • Collect requirements, design and implement reporting and visualization tool for use by QM and fundamental team members
  • Provide ad hoc development support to individual embedded QM researchers in customizing the tools for PM-specific needs

QM Data

  • Monitor and maintain integrity of QM data flows to ensure that QM researchers get access to timely and clean data
  • Be the first point of contact for data-processing related problems with upstream teams
  • Liaise with the Data Engineering team to on-board and maintain data sources
  • Implement business logic related to cleaning, validating and augmenting traditional and alternative data to ensure it is suitable for core QM research

QM Processes

  • Ensure proper knowledge management within the QM team including sharing of data, key analysis, insights and re-useable code
  • Oversee QM code review process to ensure proper version control, use of CI/CD pipelines, and process portability
  • Interface with Production Engineering and QI team (as needed) before QM systems are put into production

Desired Qualifications

  • 0-2 years of professional software engineering experience, including prospective graduates
  • Exceptional problem-solving skills and quick learning ability
  • Strong ability to multi-task
  • Focus on quality control, improvements, and adherence to agreed processes and controls
  • Ability to interact with and influence various stakeholders of different tenure and levels of expertise
  • Experience with coding in Python / C# / Scala / Java / Go or equivalent.
  • Experience working with a variety of data storage and manipulation tools such as SQL, Pandas, Elasticsearch & Kibana, Snowflake.
  • Experience with various ETL/ELT technologies such as Airflow / Argo / Dagster / Spark / Hive
  • Understanding and balanced view to delivering pragmatic code to deadlines vs maintaining high levels of engineering standards
  • Experience with cloud, container and micro service infrastructures (e.g., Kubernetes, Docker, Helm, AWS, GCP)
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