Careers with Prospect

Prospect is on a mission to revolutionise decision making in sport. We combine artificial intelligence with deep subject-matter expertise to answer performance and commercial challenges across the sport and media industry, supporting decision making from the field through to the boardroom. Our work spans Performance Analytics, Sporting Product Analytics, Marketing Analytics & Execution, and Consulting & Advisory, partnering with rights holders, clubs, leagues, investors and broadcasters across the industry.

We are looking for a curious, practical and service-minded Junior DevOps Engineer to join our team. You do not need previous DevOps experience: this is a development role for someone with strong technical foundations, a willingness to learn on the job and the confidence to ask good questions. Prior exposure to DevOps, cloud infrastructure or IT support would be an advantage.

You will report directly to the Lead DevOps Engineer and work closely with colleagues across our engineering, data and analytics teams, learning how Prospect's systems operate while taking growing ownership of day-to-day tasks. The role reaches beyond traditional DevOps: you will help run and improve our underlying infrastructure, largely on AWS; support deployment and engineering workflows; help colleagues make safe, effective use of LLM capabilities in line with the company's AI roadmap; and act as a first point of contact for internal IT support.

• Cloud Infrastructure and DevOps
  •   Learn to support, maintain and improve Prospect's AWS environments under guidance.
  •   Assist with infrastructure-as-code, CI/CD pipelines, containerised services and deployment workflows.
  •   Help monitor platform health, availability, security and cloud costs; investigate alerts and incidents with support.
  •   Support identity and access management, secrets, certificates, backups and disaster-recovery processes using least-privilege principles.
  •   Maintain clear runbooks, technical documentation and operational checklists.
  •   Gradually identify repetitive work and help automate it with scripts, workflows or internal tooling.
• AI Enablement
  •  Work with the wider engineering team to help deliver the company's AI roadmap.
  •  Help colleagues use approved LLM tools safely and effectively in day-to-day work.
  •  Support experiments, integrations and internal tools that apply LLM capabilities to engineering and business workflows.
  •  Gather feedback and usage insights, document good practices and help turn successful experiments into reliable, maintainable processes.
  •  Consider privacy, security, quality and human oversight when supporting AI-enabled workflows.
• IT Administration and Colleague Support
  •  Support the technical onboarding and offboarding of colleagues, including accounts, access, laptops and equipment.
  •  Help administer Microsoft 365, Entra ID/Azure AD, Intune, SharePoint, GitHub, Jira and other company systems under  guidance.
  • Act as a friendly first point of contact for general internal IT and technology questions, escalating issues when appropriate.  Maintain accurate device, software and access records and support the laptop lifecycle.
  •  Help improve self-service guidance and make recurring support processes more consistent and efficient.
• Team Contribution and Development
  •  Work closely with Data Engineering, Data Science, Analytics, Product and other teams to understand their infrastructure and tooling needs.
  •  Participate in Agile ceremonies and keep Jira tickets, documentation and time records current.
  •  Take ownership of well-scoped tasks, communicate blockers early and build independence over time.
  •  Pair with experienced colleagues, apply feedback and share useful lessons with the wider team.

• Be curious and eager to learn, with a genuine interest in cloud technology, automation and AI.
• Be a practical problem solver who is comfortable investigating unfamiliar issues and asking for help when needed.
• Be service-minded, patient and approachable when supporting colleagues with different levels of technical experience.
• Be reliable and security-conscious, particularly when handling accounts, permissions, devices and company data.
• Communicate clearly, document work and keep colleagues informed of progress and blockers.
• Be collaborative, open to feedback and willing to share what they learn.
• Have a passion for the business of sports, media and technology.

• Around 2+ years’ experience in a technical, IT or cloud-adjacent role (including study, personal projects, internships or a first role), with clear evidence of technical curiosity and self-directed learning.
• Basic understanding of operating systems, networking and how web or cloud applications fit together.
• Some familiarity with Git and at least one scripting or programming language, such as Python, Bash or PowerShell.
• A structured approach to troubleshooting and the ability to explain technical issues clearly.
• Strong organisation and attention to detail.
• Willingness to learn AWS, infrastructure-as-code, CI/CD, containers, monitoring, identity and security practices.
• Interest in LLMs and enthusiasm for helping others use new technology responsibly.

Useful but not required

• Exposure to AWS or another cloud platform.
• Familiarity with GitHub Actions, Terraform, Docker, Linux or the AWS CLI.
• Experience providing IT support or administering Microsoft 365, Entra ID/Azure AD, Intune or SharePoint.
• Experience experimenting with LLM APIs, AI coding tools, agents, retrieval systems or workflow automation.
• Any relevant certification, home lab, open-source contribution or portfolio project.

As part of Prospect, you will benefit from:

• 25 days of annual leave, plus Bank Holidays and office closure over the Christmas period.
• Bupa private medical insurance cover for employees.
• Monthly socials and opportunities to expand your network.
• A hybrid working pattern.
• Two weeks of “work from anywhere” during August.
• A start-up culture where you can make a real impact, learn quickly and work in a fast-paced environment.
• Ongoing training and upskilling opportunities.

In this role, you will also build practical experience across secure AWS infrastructure, CI/CD, infrastructure-as-code, observability, access management, disaster recovery, AI-enabled workflows and internal IT support.

A junior, development-focused opportunity for someone with strong technical foundations and a desire to grow across DevOps, cloud infrastructure, AI enablement and internal IT support. Working closely with the Lead DevOps Engineer and teams across Engineering, Data and Analytics, the successful candidate will build practical experience with AWS, automation, deployment workflows, security, LLM-enabled tools and colleague support while taking on increasing ownership over time.

Prospect is committed to providing equal opportunities for candidates from all backgrounds and to building an inclusive working environment where diversity is celebrated.

Prospect is on a mission to revolutionise decision making in sport and to build the world’s best sports analytics and consulting teams and products. We work across performance and commercial challenges, combining deep sporting knowledge with data, technology and practical advice. Our clients and partners include rights holders, clubs, investors, broadcasters and other organisations across the sports industry.

We are hiring Data Engineers at Junior, Mid-Level and Senior levels who are excited by the intersection of sport, analytics and technology. You will work in cross-functional teams to turn complex and varied data into trusted, maintainable products that support decisions from the field to the boardroom. The scope and ownership of the role will reflect your level of experience, from developing strong practical foundations at Junior level, through independent delivery at Mid-Level, to architecture and technical leadership at Senior level.

Our platform is largely cloud-based, with AWS at its core and technologies including Snowflake, Databricks, dbt, Spark, Kedro and Python used where appropriate. We do not expect every candidate to know every tool. We value sound engineering foundations, evidence of learning and the ability to choose and apply technology thoughtfully.

Junior Data Engineer

• Support the development and operation of reliable, scalable data pipelines under guidance.
• Ingest, clean and transform structured and unstructured data from multiple sources.
• Assist with testing, validation, monitoring and documentation of data assets.
• Investigate data-quality or pipeline issues and communicate findings clearly.
• Contribute production-quality Python and SQL through version-controlled workflows.
• Learn Prospect’s AWS platform, data stack, security practices and delivery standards.
• Collaborate with Data Science, Analytics, Product and engineering colleagues.

Mid-Level Data Engineer

• Independently design, build and maintain reliable data pipelines and reusable data products.
• Manage and optimise cloud storage, processing and warehouse workloads.
• Define appropriate data models, interfaces, tests, monitoring and documentation.
• Own workstreams from technical design through deployment and operational support.
• Diagnose performance, reliability and data-quality issues and implement durable fixes.
• Apply secure engineering, CI/CD and infrastructure practices in collaboration with platform colleagues.
• Review code and support the development of junior colleagues.

Senior Data Engineer

• Architect secure, scalable and maintainable data platforms, products and workflows.
• Lead technical design and guide the evolution of Prospect’s data architecture.
• Establish engineering standards for data quality, testing, observability, governance and documentation.
• Lead complex, multi-team delivery and make pragmatic build-versus-buy and technology decisions.
• Improve platform performance, resilience, security and cost efficiency.
• Partner with Data Science, Analytics, Product, DevOps and client teams on solution design.
• Mentor engineers, raise technical capability and contribute to hiring and career development.

AI and LLM-Enabled Engineering

At every level, data engineers may contribute to Prospect’s AI roadmap by building reliable data foundations for LLM-enabled products and internal workflows.

• Contribute to ingestion and retrieval pipelines for LLM-enabled products and internal workflows.
• Support metadata and access controls, evaluation datasets, monitoring and cost management.
• Support the safe integration of model APIs, applying sound data-engineering and responsible-use principles.

• Be highly motivated and results-driven, with a passion for technology and data.
• Be a practical problem solver who takes ownership and communicates blockers early.
• Be curious and willing to learn unfamiliar tools, domains and data.
• Be collaborative, open to feedback and comfortable working across disciplines.
• Be security- and quality-conscious when handling client and company data.
• Be able to explain technical decisions clearly to technical and non-technical colleagues.
• Have a passion for the business of sports, media and technology.

• Junior Data Engineer
  • Foundational Python and SQL skills demonstrated through study, personal projects, an internship, a first role or another relevant route.
  • Basic understanding of databases, data modelling and how data moves through a system.
  • Familiarity with Git, testing and documentation, or a willingness to learn them quickly.
  • A structured approach to troubleshooting and strong attention to detail.
  • Eagerness to learn cloud services and tools such as AWS, Snowflake, Databricks, dbt, Spark and Kedro.
  • Clear communication, curiosity and openness to pairing and feedback.
  • Prior professional data-engineering or AWS experience is useful but not required.

  Mid-Level Data Engineer

  • Strong Python and SQL skills applied to production data systems.
  • Demonstrated experience designing and operating batch or streaming pipelines.
  • Working knowledge of cloud data services, preferably on AWS.
  • Experience with relevant technologies such as Snowflake, Databricks, dbt, Spark, Kedro or comparable tools.
  • Sound practices in data modelling, automated testing, observability, version control and CI/CD.
  • Ability to balance delivery speed with reliability, maintainability, security and cost.
  • Confident communication with technical colleagues, analysts and project stakeholders.

  Senior Data Engineer

  • Deep expertise in distributed data architecture, orchestration and production data operations.
  • Advanced Python and SQL skills and strong knowledge of data modelling and platform design.
  • Demonstrated leadership of cloud data platforms, preferably using AWS and technologies such as Snowflake, S3, Athena, Databricks, dbt or Spark.
  • Expertise in security, governance, observability, performance and cost optimisation.
  • Strong understanding of CI/CD, infrastructure-as-code and reliable software-delivery practices.
  • Ability to translate ambiguous business needs into clear technical direction.
  • A track record of mentoring others and influencing engineering standards across teams.

As part of Prospect, you will benefit from:

• 25 days of annual leave, plus Bank Holidays and office closure over the Christmas period.
• Bupa private medical insurance cover for employees.
• Monthly socials and opportunities to expand your network.
• A hybrid working pattern.
• Two weeks of “work from anywhere” during August.
• A start-up culture where you can make a real impact, learn quickly and work in a fast-paced environment.
• Ongoing training and upskilling opportunities.

An opportunity to join Prospect’s Data Engineering team at Junior, Mid-Level or Senior level, depending on experience. Across the three levels, the team builds and operates reliable data pipelines and cloud-based data products, with increasing ownership from supported delivery at Junior level, through independent workstream ownership at Mid-Level, to architecture, technical standards and mentoring at Senior level. The successful candidates will work across Prospect’s AWS-centred data platform and collaborate closely with Data Science, Analytics, Product, DevOps and other teams.

Prospect is committed to providing equal opportunities for candidates from all backgrounds and to building an inclusive working environment where diversity is celebrated.

Prospect is on a mission to revolutionise decision making in sport and to build the world’s best sports analytics and consulting teams and products. We work across performance and commercial challenges, combining deep sporting knowledge with data, technology and practical advice. Our clients and partners include rights holders, clubs, investors, broadcasters and other organisations across the sports industry.

We are hiring Data Scientists at Junior, Mid-Level and Senior levels who are excited by the intersection of sport, analytics and technology. You will work in cross-functional teams to turn complex questions into rigorous analysis, models and products that improve decisions from the field to the boardroom. You will lead analytical workstreams from problem definition through deployment and evaluation, selecting appropriate methods and translating results into practical recommendations.

The role combines statistical thinking, machine learning, software engineering and communication. Depending on the problem, you may work on forecasting, classification, optimisation, simulation, experimentation, causal analysis, generative AI or decision-support tools. We value sound reasoning and measurable impact over use of any particular technique.

Junior Data Scientist

• Support the design, development and validation of analytical and machine-learning solutions.
• Clean, explore and analyse complex datasets using reproducible workflows.
• Contribute well-structured, tested Python code to shared codebases.
• Evaluate model performance, limitations and data quality under guidance.
• Produce clear visualisations, written insights and client-facing presentations.
• Support deployment, monitoring and documentation of analytical outputs.
• Collaborate with engineers, analysts and domain experts to deliver useful solutions.

Mid-Level Data Scientist

• Lead analytical workstreams from problem definition through deployment and evaluation.
• Build, validate and monitor predictive, prescriptive or generative-AI solutions.
• Select appropriate methods and define success metrics with stakeholders.
• Translate technical results and uncertainty into practical recommendations.
• Write maintainable, tested code and work with engineers to productionise solutions.
• Design experiments or evaluation approaches and diagnose model or data drift.
• Review work and support the development of junior colleagues.

Senior Data Scientist

• Lead complex, multi-stream analytical projects and shape modelling strategy.
• Define standards for experimentation, validation, evaluation, coding and responsible AI.
• Guide the design and productionisation of machine-learning, optimisation and AI-enabled products.
• Frame ambiguous client or product questions and identify the highest-value analytical approach.
• Partner with Data Engineering, DevOps, Analytics and Product on scalable solution architecture.
• Communicate recommendations, uncertainty and risk to senior client and internal stakeholders.
• Mentor team members, raise technical capability and contribute to hiring and career development.

AI and LLM Capabilities

Data scientists at every level may contribute to Prospect’s AI roadmap by identifying valuable use cases and developing LLM-enabled products or internal workflows. Prior LLM experience is useful but is not a substitute for strong experimental design and statistical judgement.

• Contribute to prompt and context design, retrieval-augmented generation, agents and tool use, and structured outputs where appropriate.
• Support model and vendor selection, evaluation, monitoring, guardrails, privacy and cost management.
• Evaluate generative systems rigorously, recognise failure modes and keep appropriate human oversight in place.

• Be intellectually curious, rigorous and motivated by solving real problems.
• Be comfortable challenging assumptions and communicating uncertainty.
• Be a practical self-starter who takes ownership and communicates blockers early.
• Be collaborative, open to feedback and willing to learn from other disciplines.
• Be thoughtful about data quality, privacy, fairness and responsible use of AI.
• Be able to explain technical work clearly to clients and non-technical stakeholders.
• Have a passion for the business of sports, media and technology.

Junior Data Scientist

• Foundational Python and statistical skills demonstrated through study, personal projects, an internship, a first role or another relevant route.
• Familiarity with common data-science libraries such as pandas, NumPy, scikit-learn or comparable tools.
• Basic understanding of model validation, uncertainty and the difference between correlation and causation.
• Ability to explore data carefully and communicate findings clearly.
• Familiarity with Git, testing and documentation, or a willingness to learn them quickly.
• Curiosity, attention to detail and openness to pairing and feedback.
• Prior professional data-science or AWS experience is useful but not required.

Mid-Level Data Scientist

• Strong Python, statistical-modelling and machine-learning skills applied to real problems.
• Demonstrated experience with relevant supervised, unsupervised, forecasting, optimisation, simulation or causal methods.
• Sound understanding of validation, leakage, bias, uncertainty and model monitoring.
• Experience with production software practices including version control, testing, code review and CI/CD.
• Working knowledge of cloud platforms, preferably AWS, and the lifecycle of deployed models.
• Strong written, visual and verbal communication with technical and client stakeholders.
• Ability to balance analytical sophistication with delivery time, maintainability and business value.

Senior Data Scientist

• Deep expertise in relevant areas of statistics, machine learning, optimisation, simulation, causal inference or generative AI.
• Advanced Python and strong software-engineering practices for production analytical systems.
• Demonstrated leadership of model development, deployment, monitoring and continuous improvement.
• Strong understanding of MLOps, cloud deployment, data architecture and operational reliability.
• Expertise in evaluation design, model risk, privacy, fairness and responsible AI governance.
• Ability to make clear technical decisions under ambiguity and connect analytical work to measurable impact.
• A track record of mentoring others and influencing technical standards across teams.

As part of Prospect, you will benefit from:

• 25 days of annual leave, plus Bank Holidays and office closure over the Christmas period.
• Bupa private medical insurance cover for employees
• Monthly socials and opportunities to expand your network.
• A hybrid working pattern.
• Two weeks of “work from anywhere” during August.
• A start-up culture where you can make a real impact, learn quickly and work in a fast-paced environment.
• Ongoing training and upskilling opportunities.

An opportunity to join Prospect’s Data Science team at Junior, Mid-Level or Senior level, depending on experience. Across the three levels, the team turns complex questions into rigorous analysis, models and products, with increasing ownership from supported development and validation at Junior level, through end-to-end analytical workstream leadership at Mid-Level, to modelling strategy, technical standards and mentoring at Senior level. The successful candidates will work cross-functionally with engineering, analytics, product and domain experts and may contribute to Prospect’s AI roadmap.

Prospect is committed to providing equal opportunities for candidates from all backgrounds and to building an inclusive working environment where diversity is celebrated.