Senior Data Scientist - MLOps

This job is with Skyscanner, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. We are looking for a Senior Data Scientist to join our Machine Learning Enablement team at Skyscanner. This team plays a crucial role in scaling the adoption of AI and Machine Learning technologies by helping data science and engineering teams deploy production-grade models efficiently and reliably.

Sitting at the intersection of data science and engineering, you will enable teams to build robust ML pipelines, ensure production readiness, and drive best practices in MLOps. You will also collaborate closely with the ML platform team, advocating for the adoption of cutting-edge tools such as feature stores and ML observability solutions. As a Senior Data Scientist focused on model deployment, you will work with a diverse range of teams, ensuring that our machine learning models are scalable, reliable, and well-governed, ultimately contributing to Skyscanner's AI-driven future!

What You’ll Do:

• Support machine learning teams in deploying models to production, ensuring reliability, scalability, and adherence to best practices.
• Assist in monitoring and improving deployed ML models, mitigating risks, and optimising performance.
• Work closely with the ML platform team to refine and advocate for best practices in using platform tools such as feature stores, model registries, and observability solutions.
• Guide teams in implementing model deployment pipelines that meet regulatory and internal governance standards.
• Act as a bridge between data science teams and platform engineers, fostering a culture of MLOps excellence.
• Identify and address bottlenecks in model inference and retraining pipelines, improving reliability and cost efficiency.
• Assist in diagnosing and resolving production incidents related to ML deployments, continuously improving system robustness.

What We’re Looking For:

• Previous experience as a Senior Data Scientist, ML engineering, or a related field, with hands-on experience deploying machine learning models in production.
• Strong understanding of ML models, how they work, and when to apply them effectively.
• Proficiency in Python and SQL, with experience in Apache Spark & Airflow (ideal but not required).
• Hands-on experience with ML frameworks (TensorFlow, PyTorch, Scikit-Learn) and cloud platforms (AWS, GCP, or Azure).
• Familiarity with containerisation technologies (Docker, Kubernetes).
• Experience working with CI/CD pipelines, model registries, and ML observability tools.
• Understanding of responsible AI principles, model monitoring, and data privacy best practices.
• Ability to work cross-functionally with data scientists, engineers, and business stakeholders to drive ML deployment excellence.
• Naturally curious and inquisitive - beyond just modelling, you’re interested in data quality, business impact, and system interactions.
• Fluent in English, with the ability to communicate effectively across different levels of management and technical domains.

What else can we offer you...

You’ll join a brilliantly diverse group from all corners of the world. After all, travel is about finding new perspectives and experiencing new people and cultures - and Skyscanner is strongest when our teams are both inclusive and diverse. We recognise and challenge everyday biases, remove obstacles to inclusion and ensure all our people can thrive and be themselves.

Skyscanner is a hybrid working company and most roles can be either Full Time or Part Time. We believe when people meet regularly in person, we are better able to innovate, learn, collaborate and inspire. We ask people to be in the office on average 8 days per month.

Already a global leader in travel, we want to elevate the way we work to a whole other level. In return, you’ll get meaningful things like medical insurance, headspace subscriptions, a home office allowance and the option to buy more holiday. You’ll have the opportunity to work from any country for 4 weeks a year, and 30 days in our other global offices. Everything, in other words, to help you relax and give your best.