Principal Data Scientist

Are you excited to apply advanced analytics and machine learning to real‑world aviation challenges?

Would you like to shape best practices and mentor others while remaining hands‑on with high‑impact data science work?

About the Business
At Cirium, our goal is to keep the world connected. We are the industry leader in aviation analytics; helping our customers understand the past, present, and predicting what will happen tomorrow. Our mission is to transform the aviation industry by enabling airlines, airports, travel companies, tech giants, aircraft manufacturers, financial institutions, and many more accelerate their own digital transformation.

About our Team
Propulsion Labs is Cirium’s innovation group, focused on solving complex, highimpact problems in aviation analytics. We work in small, collaborative teams to explore ideas, test solutions, and scale products that deliver meaningful value to customers. Our environment supports autonomy, learning, and inclusive collaboration.

About the Role
As a Principal Data Scientist, you will be a senior individual contributor who brings deep technical expertise while shaping best practices across the data science community. You will work closely with partners across product, engineering, and the business to design and deliver data‑driven solutions. This role combines technical leadership, collaboration, and hands‑on development to solve complex, real‑world problems in aviation analytics.

Responsibilities

• Act as a technical authority for advanced analytics, machine learning, and generative AI, defining and evolving best practices for scalable and robust solutions
• Provide thought leadership on emerging technologies and methodologies relevant to aviation analytics
• Mentor and coach data scientists, supporting continuous learning and technical excellence
• Collaborate with product managers, engineers, and domain experts to align data science initiatives with business goals
• Translate complex technical insights into clear, actionable recommendations for stakeholders
• Lead by example through hands‑on coding, modelling, and solution design
• Design, prototype, and validate innovative approaches to high‑impact problems
• Partner with engineering teams to deploy models into production environments

Requirements

• Strong experience working with large, complex, real‑world datasets, ideally in aviation or related industries
• Expertise in data wrangling, feature engineering, and building scalable data pipelines
• Deep expertise in at least one advanced analytics area, with experience across several of the following: predictive modelling on tabular data, deep learning, NLP and large language models, generative AI pipelines, simulations, graph‑based models, time‑series forecasting, geospatial modelling, causal inference, reinforcement learning, optimisation, or anomaly detection
• Proven experience deploying, or partnering to deploy, customer‑facing machine learning systems into production
• Ability to collaborate across disciplines and communicate technical concepts to non‑technical audiences

Risk benefit statement

Learn more about the LexisNexis Risk team and how we work here

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