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hudl
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Великобритания
Зарплата
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LeadГибридПолная занятость

Lead Data Scientist

Оценка ИИ

Высокий балл за сильный бренд компании в индустрии SportsTech, прозрачный диапазон зарплаты и отличную корпоративную культуру. Возможность удаленной работы и фокус на work-life balance делают вакансию крайне привлекательной.


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Сложность вакансии

ЛегкоСложно
Оценка ИИ

Роль требует не только глубоких знаний в области ML и статистики, но и навыков лидерства для управления дорожной картой исследований и менторства команды. Дополнительную сложность придает специфика работы со спортивными данными (event/tracking data) и необходимость написания продакшн-кода.

Анализ зарплаты

Медиана100 000 £
Рынок85 000 £ – 135 000 £
Оценка ИИ

Предложенный диапазон (£76k - £127k) полностью соответствует рыночным стандартам Лондона для позиции Lead Data Scientist. Нижняя планка подходит для кандидатов, только переходящих на уровень Lead, в то время как верхняя граница является конкурентной для опытных экспертов.

Сопроводительное письмо

I am writing to express my interest in the Lead Data Scientist position at Hudl. With a strong background in quantitative disciplines and extensive experience in implementing machine learning models for complex systems, I am excited about the opportunity to lead the Global Football Metrics team. My expertise in Python and statistical modeling, combined with a passion for sports analytics, aligns perfectly with Hudl's mission to help teams see their game differently.

Throughout my career, I have demonstrated a commitment to technical excellence and mentorship. I have a proven track record of collaborating with cross-functional teams to deliver scalable data pipelines and innovative metrics. I am particularly drawn to Hudl's culture of autonomy and continuous learning, and I am eager to contribute to the development of cutting-edge tactical and recruitment insights that impact coaches and athletes globally.

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Откликнитесь в hudl уже сейчас

Присоединяйтесь к Hudl, чтобы создавать будущее спортивной аналитики и работать с данными мирового уровня!

Описание вакансии

At Hudl, we build great teams. We hire the best of the best to ensure you’re working with people you can constantly learn from. You’re trusted to get your work done your way while testing the limits of what’s possible and what’s next. We work hard to provide a culture where everyone feels supported, and our employees feel it—their votes helped us become one of Newsweek's Top 100 Global Most Loved Workplaces.  

We think of ourselves as the team behind the team, supporting the lifelong impact sports can have: the lessons in teamwork and dedication; the influence of inspiring coaches; and the opportunities to reach new heights. That’s why we help teams from all over the world see their game differently. Our products make it easier for coaches and athletes at any level to capture video, analyze data, share highlights and more.

Ready to join us?

Your Role

We’re looking for a Lead Data Scientist to join our Global Football Metrics data science team, which is focused on delivering innovative machine learning models to keep Hudl at the cutting edge of sports analytics.

As a Lead Data Scientist, your key responsibilities will include:

  • Work with a cross-functional team. You’ll collaborate with Engineering, Quality Assurance, Product, Design and Scrum disciplines to deliver cutting-edge tactical and recruitment insights.
  • Develop and deliver. You will have access to industry-leading data from a variety of sources, and will lead the research and development roadmap for new Global Football models and metrics.
  • Test new ideas. At Hudl, we iterate rapidly, deploying changes to the product hundreds of times daily across our Engineering team. In addition to working on concrete metrics, you’ll contribute to the implementation of scalable data pipelines and associated orchestration and monitoring tools.
  • Mentor. You’ll share your expertise and educate others on development best practices and trade-offs, setting an example in planning, designing and delivering complex projects, and maintaining high standards of statistical rigour.

We’d like to hire someone for this role who lives near our office in London, but we’re also open to remote candidates. Remote candidates would have the ability to work from a co-working space or their home.

Must-Haves

  • Technical expertise. You have a strong background in a quantitative discipline, and proven experience implementing statistical and machine learning models for statistical inference in complex systems.
  • A team player. You understand that problem-solving is a team effort and will help others on our Engineering team learn and develop their skills.
  • User-focused. You’re excited to have your work used by real people to solve real problems.
  • Willing to learn. You have solid engineering skills but are always willing to dive into specific areas to gain the expertise needed to be successful in your role.

Nice-to-Haves

  • Sports industry experience. If you’ve worked with event or tracking data previously, that’s a plus.
  • Tech stack knowledge. Our tech stack is Python, PostgreSQL and Redshift. We will consider strong candidates with experience of R or Stan, but prefer those with more full stack skills and capable of writing production code.

Our Role

  • Champion work-life harmony. We’ll give you the flexibility you need in your work life (e.g., flexible vacation time above any required statutory leave, company-wide holidays and timeout (meeting-free) days, remote work options and more) so you can enjoy your personal life too.
  • Guarantee autonomy. We have an open, honest culture and we trust our people from day one. Your team will support you, but you’ll own your work and have the agency to try new ideas.
  • Encourage career growth. We’re lifelong learners who encourage professional development. We’ll give you tons of resources and opportunities to keep growing.
  • Provide an environment to help you succeed. We've invested in our offices, designing incredible spaces with our employees in mind. But whether you’re at the office or working remotely, we’ll provide you the tech you need to do your best work.
  • Support your wellbeing. Depending on location, we offer medical and retirement benefits for employees—but no matter where you’re located, we have resources like our Employee Assistance Program and employee resource groups to support your mental health.

Compensation

The base salary range for this role is displayed below—starting salaries will typically fall near the middle of this range.

We make compensation decisions based on an individual's experience, skills and education in line with our internal pay equity practices.

This role will also be eligible for a long-term incentive (LTI) award. Any bonuses awarded are based on individual and company performance paid at Hudl's discretion.

Base Salary Range

 £76,000 - £127,000 GBP

Inclusion at Hudl

Hudl is an equal opportunity employer. Through our actions, behaviors and attitude, we’ll create an environment where everyone, no matter their differences, feels like they belong. 

We offer resources to ensure our employees feel safe bringing their authentic selves to work, including employee resource groups and communities. But we recognize there’s ongoing work to be done, which is why we track our efforts and commitments in annual inclusion reports

We also know imposter syndrome is real and the confidence gap can get in the way of meeting spectacular candidates. Please don’t hesitate to apply—we’d love to hear from you.

Privacy Policy

Hudl Applicant and Candidate Privacy Policy

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Навыки

  • Python
  • Machine Learning
  • Statistics
  • PostgreSQL
  • Data Pipelines
  • R
  • Amazon Redshift
  • Stan

Возможные вопросы на собеседовании

Для лида важно понимать, как технические метрики соотносятся с ценностью для конечного пользователя (тренеров и аналитиков).

Как бы вы подошли к разработке новой метрики для оценки эффективности футболиста, чтобы она была одновременно статистически обоснованной и понятной для тренера?

Проверка навыков проектирования систем и работы с данными в реальном времени.

Опишите ваш опыт проектирования масштабируемых конвейеров данных (data pipelines). Какие инструменты мониторинга и оркестрации вы предпочитаете использовать и почему?

Оценка лидерских качеств и способности развивать команду.

Расскажите о случае, когда вам пришлось менторить коллегу или внедрять стандарты разработки в команде. С какими трудностями вы столкнулись?

Проверка умения работать в междисциплинарной среде.

Как вы расставляете приоритеты в исследовательских задачах, когда требования от продуктовой команды конфликтуют с техническими ограничениями или нехваткой данных?

Проверка владения основным стеком компании.

В чем, по вашему мнению, основные преимущества и недостатки использования Python по сравнению с R или Stan при развертывании моделей машинного обучения в продакшн?

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hudl
Страна
Великобритания
Зарплата
76 000 ₽ – 127 000 ₽