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Quant Model Engineer

Python Model Engineer - Sportsbook #LI-ML1 #LI-Hybrid

The role

The Quant Team is a fast-growing team within the broader Data Science function focused on developing, productionising and deploying quantitative sports models. The team is an important component of the vision for the future evolution of the Kindred Sportsbook Platform (KSP).

We are currently looking to recruit an experienced Model Engineer to work on modelling frameworks and deployment pipelines to help us to scale efficiently and iterate rapidly following best practices. The successful candidate will work closely with analysts, architects, MLOps engineers and other key stakeholders in cross-functional product teams.

What you will do

We expect that performing this role you will:

  • Plan and build components of model fitting frameworks working closely with architects, analysts and the Quant Engineering Manager.
  • Refine and optimize existing quantitative sports models and tooling.
  • Build scalable tools and apps to facilitate model evaluation and promote rapid iteration and quick feedback loops.
  • Build robust APIs and interfaces to allow controlled access to Quant Team data products.
  • Work with relevant stakeholders to contribute to the design and implementation of production components within Quant Team products.
  • Contribute to the deployment of developed software applications, using CI/CD, containerisation and DevOps approaches and best practices.
  • Support product maintenance and monitoring initiatives.
  • Adopt an agile approach to writing high-quality code that follows software engineering best practices and facilitates collaboration and re-usability by other team members.

 

About you

We think that to be successful in this role you will be able to demonstrate many of the following attributes:

  • Building and/or deploying statistical or machine learning models in a production environment.
  • Applied experience demonstrating specialism in at least one of the following:
    • Building robust production quality APIs.
    • Platform and tooling optimisation.
    • Designing and building frameworks used for model evaluation  and/or back-testing.
  • 2-4 years commercial experience in a similar role.
  • Strong Python skills.
  • Experience developing applications that form part of a quantitative or data science lifecycle.
  • Experience deploying software into containerised environments.
  • Excellent communication ability, both written and verbal, able to explain complex topics to non-specialists.
  • A problem-solving growth mindset with the ability to pick up new tools and concepts quickly.
  • Open-mindedness, able to interact in a constructive manner with the Quant team, stakeholders and other contributors to Quant solutions.
  • Passionate about your personal development and upskilling.
  • Ability to deal with and account for uncertainty.
  • Ability to make well informed decisions based on data and to prioritise effectively.

In addition, having any of the following would be an advantage:

  • Practical MLOps experience.
  • Experience deploying containerised applications onto Kubernetes.
  • Experience writing and deploying software within cloud-based environments, ideally AWS.
  • Experience with frameworks and technologies used in component orchestration, including Airflow or equivalent.
  • Experience working with sporting data or demonstrable knowledge and interest in this area, including an interest in sports betting.
  • Good knowledge of additional coding languages, and in particular a compiled language.
  • Masters degree or PhD in STEM subject.

 

Application process

Click on the "Apply Now" button and complete the short web form. Please add a covering letter in English to let us know your motivation for applying and your salary expectation. Our Talent Acquisition team will be in touch soon.

Kindred is an equal opportunities employer committed to employing a diverse workforce and an inclusive culture. As such we oppose all forms of discrimination in the workplace. We create equal opportunities for all our applicants and will treat people equally regardless of and not limited to, gender, age, disability, race, sexual orientation. We are committed not only to our legal obligations but also to the positive promotion that equal opportunities bring to our operations as set out in our sustainability framework.

 

Job alerts

Not suited to this role but interested in working at Kindred Group?

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Location
London
Kindred House, 17-25 Hartfield Road, Wimbledon, London, United Kingdom, SW19 3SE
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  • Office:
    London
  • Type of Employment:
    Full Time Permanent
  • Reference Number:
    TEC1907
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