Newcastle United is now looking for a chief scientist.
The club made this audience in advertising (see below) to identify the right person for the role.
Newcastle United explains; “We are looking for a curious and driven scientist to help push the boundaries of football information. Working with advanced data to discover insights that form the recruitment, coaching and strategy.”
Good luck with those who get the job!
Training Ground Guru site Advertise this position on NUFC – 8 September 2025:
Newcastle United – Main scientist
About the club
We are the heartbeat of the city. Come and be part of a long and proud history in which we strive to be the best in everything we do, on and next to the field. Bring the people and communities together, becomes a member of the Newcastle United family, while we start the next steps of our exciting journey.
Over the role
Are you passionate about football and machine learning? At Newcastle United we are looking for a curious and driven scientist to help us push the limits of football information. You work with advanced data, from following players and skeletal movements to event and positional feeds, to discover insights that determine recruitment, coaching and strategy.
In this role you will design and implement models those decisions of players evaluate, predict movement and assess possession value. You will work closely with our data team and senior technical staff to convert complex data sets into clear, usable insights that match our football philosophy.
Your impact
We are looking for someone who knows when a well -coordinated statistical model should be used and when it is deeply unleashed. You will work comfortably with large -scale series of time, custom -made statistics and integrate different models to create a rich, layered analysis. If you have worked with football data providers such as Statsbomb or Second Spectrum, even better.
You need a master or doctorate in a quantitative field, strong Python skills and extensive experience with applying Core Machine Learning libraries such as Scikit-Learn, XGBOOST or LIGHTGBM on structured data problems. The role can also mean that the development and implementation of in -depth learning models with the help of frameworks such as Pytorch or TensorFlow where necessary. Experience with version management (GIT) and applying best practices for software engineering is essential. Familiarity with Mlops principles and the integration of cloud-based data platforms such as Snowflake and Azure is a plus.
About the team
We build something special here – and we want someone who is enthusiastic to be part of and ready to help us stay in the foreground in football analyzes.
Location
This role is located in Newcastle on Tyne, with an expectation of three days a week from the office. Keep in mind that if you are not in the northeast of England and are unable to move, we regret that we cannot continue your application.
Why choose us?
We have a series of great benefits and rewards, of flexible ways of working, participation in our non-contractual employee bonus schedule, NUFC Life Assurance, Free Parking, Discount at Shearers Bar and the Club Shop, and helping the Hand-where you have access to free general practitioners’ appointments, Welfare and Financial Support, Free Lunch and Financial Support. Moreover, we have a salary sacrificial schedule that includes, technology, car, cycling to work and much more.
Summary
Responsible for the development of data -driven models to improve football information and performance analysis. Design and implement solutions for machine learning using event, tracking, positional and skeleton data; With a focus on evaluating decisions of players, modeling possession value, predicting player programs and ranking performance and behavior. Work closely with the data department and senior technical staff to translate complex data into usable insights for recruitment, coaching and strategy.
Role responsibilities
Design and implement statistical and machine learning models to support important football activities and decisions.
Apply a series of techniques, including guidance learning, non -controlled learning and learning deep reinforcement, with a clear understanding of their strengths and limitations in a football context.
Show a strong understanding of when well-coordinated statistical approaches (for example GLMs, Bayesian methods) must be applied to more complex machine learning models, in particular in cases where interpretability, transparency and domain lines are essential.
Develop tailor -made performance statistics using new and advanced football data (eg estimate of the body position and how this information improves in the current state of art spacecraying models).
Evaluate model performance with the help of suitable statistics and techniques, including working with applied experts to ensure that the results are interpretable, explainable and football relevant.
Integrate various ML models to generate layered, context-rich insights
Support, where necessary, preparing and structuring complex football datas sets by cleaning, transforming and combining video, tracking, event and positional data
Design meaningful functions that record spatiotemporal and contextual aspects of player and team behavior to lean models for Power Machine
Work together with football analysts and used practitioners to ensure that solutions tackle real performance questions, tailored to football philosophy and tactical requirements
Stay informed of academic and industrial progress in machine learning and sports analyzes; Ensure that Newcastle United is paramount in a fast -moving industry
Other reasonable tasks.
Roll requirements
Perform tasks at all times with regard to club policy and procedures and legislative requirements
Ensure the implementation of health and safety, security policy, the club’s welfare and equality and equality to create a safe working environment for everyone
Ensure that working methods comply with relevant legislation and legislation in the field of data protection and/or general data protection regulations (GDPR)
Follow CPD training and/or additional training Continu Professional Development (CPD) as identified or as required.
Qualifications and training
Essential:
Master of Ph.D. In a quantitative discipline such as mathematics, statistics, data science, astrophysics or related areas.
Strong competence in Python with advanced data frame techniques (EC Polars) and interactive environments such as Jupyter Notebook.
Desirable:
Experience with statistical modeling expertise in Julia or R.
Knowledge, skills and experience
Essential:
Provided ability to lead ML projects from draft development to commitment, to stimulate innovation within their expertise area.
Good understanding of version management software (ie: GIT) and software development best practice
Experience with working with large -scale time series datas sets.
Experience with time and estimate of the resources for long -term projects that can be carried out in combination with head of data and insights and technical director.
Demonstrable experience with the development and implementation of deep learning models with the help of frameworks such as Pytorch, TensorFlow, Scikit-Learn, Keras etc.
Experience with processing advanced football data, such as event data (eg Statsbomb, Impect) and tracking data (eg Second Spectrum, Cilles Corner).
Understand the challenges of working with complex, unstructured data and transform it effectively into meaningful insights that are tailor -made for stakeholders of football about coaching, recruitment and management.
Strong understanding of the technical, tactical and physical demands of football, gained through experience with a professional club and/or a sports analysis organization.
Desirable:
Good understanding of implementing Mlops principles (eg CI/CD for ML, model version, monitoring) to guarantee scalable and maintained machine learning systems. Competent Snowflake with Azure Services to streamline the data pipeline and to support workflows with end-to-end machine learning.
Have good knowledge about academic and publicly available methods for football research. ‘
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