Lead Data Scientist

Job Summary:

Title: Lead Data Scientist

Location: UK - London (Remote)

Division: Marketplace

Contract Terms: Full Time Permanent

THE TEAM

You will be joining a new Data Engineering and Data Science team to help get the most value out of our real-time data streams with a focus on ensuring genuine fans have the best opportunity to buy tickets.

THE JOB

We are seeking a highly skilled Lead Data Scientist to join our team and play a critical hands on role in developing our data infrastructure and end-to-end deployment of machine learning insights. The ideal candidate will have a deep understanding of data science platforms and tools, as well as experience designing and implementing machine learning pipelines.

WHAT YOU WILL BE DOING

• Design, implement, and maintain machine learning pipelines in Databricks and AWS that enable real-time data-driven decision-making
• Work closely with the Director of Data Science and cross-functional teams to identify data requirements, define data and ML models, and develop scalable solutions to support our growing data needs
• Build, maintain and monitor data products that ensure data quality, accuracy, and availability
• Continuously monitor and evaluate data infrastructure performance, and identify opportunities for improvement and optimization
• Ensure compliance with data security and privacy policies, and implement appropriate access controls and data governance frameworks
• Oversee data scientist, and senior data scientist project execution providing senior mentoring, leadership, and guidance.
• Lead the execution and planning of large data science projects, including quality and best practices.
• Collaborate with stakeholders to understand organizational objectives and translate them into data-driven solutions.
• Contribute to the ongoing day to day running of the Data Science team, including learning pathways, best practices, and innovation.

WHAT YOU NEED TO KNOW (or TECHNICAL SKILLS)

• Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or similar technical field/experience
• Hands-on data science expertise with code-based model development e.g. R, Python
• Strong knowledge of deploying end-to-end machine learning models in Databricks utilizing Pyspark, MLflow and workflows
• Strong knowledge of data platforms and tools, including Hadoop, Spark, SQL, and NoSQL databases
• Communicate algorithmic solutions in a clear, understandable way. Leverage data visualization techniques and tools to effectively demonstrate patterns, outliers and exceptional conditions in the data
• Knowledge working with structured and unstructured data formats
• Experience designing and implementing data pipelines and ETL processes is a plus
• Good knowledge of ML ops principles and best practices to deploy, monitor and maintain machine learning models in production
• Familiarity with Git and MLflow for managing and tracking model versions
• Experience with Kafka is a big bonus
• Experience with cloud-based data platforms such as AWS or Google Cloud Platform.
• Proven track record of running large scale mission critical data infrastructure in production
• Experience with container technologies (such as Docker) and orchestration technologies (such as Kubernetes)
• Experience working in ecommerce or retail industries is a plus
• An understanding of security measures related to ML models ensuring adherence to data privacy regulations

YOU (BEHAVIOURAL SKILLS)

• Excellent problem-solving and analytical skills, with the ability to identify and resolve complex data infrastructure issues
• Proficient collaborating with business stakeholders, data engineers, and data scientists to understand their requirements to implement of machine learning models
• A deep product focused mindset, with a desire to understand your customers and what will make their use of your output easier and more efficient
• Curious by nature, enjoy looking into problems around your core tasks, experimenting with potential solutions
• Passionate about machine learning and be able to convey a thorough understanding of how data science can support better decision making

LIFE AT TICKETMASTER

We are proud to be a part of Live Nation Entertainment, the world's largest live entertainment company.

Our vision at Ticketmaster is to connect people around the world to the live events they love. As the world's largest ticket marketplace and the leading global provider of enterprise tools and services for the live entertainment business, we are uniquely positioned to successfully deliver on that vision.

We do it all with an intense passion for Live and an inspiring and diverse culture driven by accessible leaders, attentive managers, and enthusiastic teams. If you're passionate about live entertainment like we are, and you want to work at a company dedicated to helping millions of fans experience it, we want to hear from you.

Our work is guided by our values:

Reliability- We understand that fans and clients rely on us to power their live event experiences, and we rely on each other to make it happen.

Teamwork- We believe individual achievement pales in comparison to the level of success that can be achieved by a team

Integrity- We are committed to the highest moral and ethical standards on behalf of the countless partners and stakeholders we represent

Belonging- We are committed to building a culture in which all people can be their authentic selves, have an equal voice and opportunities to thrive

EQUAL OPPORTUNITIES

We are passionate and committed to our people and go beyond the rhetoric of diversity and inclusion. You will be working in an inclusive environment and be encouraged to bring your whole self to work. We will do all that we can to help you successfully balance your work and homelife. As a growing business we will encourage you to develop your professional and personal aspirations, enjoy new experiences, and learn from the talented people you will be working with. It's talent that matters to us and we encourage applications from people irrespective of their gender, race, sexual orientation, religion, age, disability status or caring responsibilities.

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