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Machine Learning Engineer (Geospatial)

CDI
London
Salaire : Non spécifié
Télétravail non autorisé
Expérience : > 5 ans

Trainline
Trainline

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Descriptif du poste

Introducing Machine Learning at Trainline 👋

Machine Learning is at the heart of Trainline’s mission to help millions of people make daily sustainable travel choices. For instance, our ML models provide state-of-the-art search capabilities on our apps, find the lowest price for millions of customers, improve user experience with generative AI and power our digital marketing capabilities, amongst other things.  For instance, our ML models provide state-of-the-art search capabilities on our apps, find the cheapest price for millions of customers, improve user experience with generative AI and power our digital marketing capabilities, amongst other things.  For instance, our ML models provide state of the art search capabilities on our apps, find the cheapest price for millions of customers, improve user experience with generative AI and power our digital marketing capabilities amongst other things. Our embedded machine learning teams responsible for delivering these products on the full end-to-end delivery lifecycle from ideation to production and collaborate closely with the wider business to help develop the understanding and impact of machine learning and AI across all areas of Trainline.  

We are looking for a geospatial data scientist to play a key role in an exciting new team within Trainline that improves the customer experience for rail passengers with accurate train tracking and mapping tools. This role will involve innovating and developing this brand-new technology, as well as working on the maintenance, monitoring, and reliability of the system and processes.  

In addition, you will collaborate with an exciting new research and development team to bring new innovations to the market. This will also involve building proof-of-concept prototypes testing their viability, as well as develop new algorithms that will be used in real-world products, benefiting millions of passengers. 

As a part of trainline you will not only receive a competitive salary and benefits, but you’ll be joining an environment where your development is a top priority. You will have the opportunity to work with fellow ML enthusiasts on large scale production systems, delivering highly impactful products that make a difference to our millions of users.  

As a Machine Learning Engineer at Trainline, you will... 🚄 

  • Improve the passenger experience by developing data engineering pipelines that improve train tracking.  
  • Work as part of the research and development team to test the viability of new products and services.  
  • Work as part of a multi-discipline team that includes data scientists and geospatial engineers to ship new features and products.  
  • Create tools, frameworks and processes that accelerate the speed, efficiency, maintenance, and reliability of services. 

Profil recherché

We'd love to hear from you if you...🔍  

  • Have an advanced degree in mathematics, physics, statistics or a similar quantitative discipline. 
  • Are experienced in designing machine learning algorithms, using processes such as feature engineering, model selection and hyperparameter tuning.   
  • Are proficient with Python data science libraries such as Jupyter Notebooks, Numpy, Pandas, Tensorflow, and SciKit Learn.  
  • Have extensive experience in using mathematical models and statistics to extract meaning from data.  
  • Are confident in visualising data and communicating insights to stakeholders who are from a range of disciplines.  
  • You have experience with geospatial analysis, using, for example network graphs and tools like QGIS and/or ArcGIS. 

Nice to have 😍

  • Experience with SQL using databases such as MySQL, PostgreSQL, Athena, Redshift. 
  • You’re familiar with cloud-based platforms like AWS and CI/CD processes using tools like GitHub Actions.  
  • Are experience with working with a range of estimation algorithms, such as those used in navigation and digital signal processing.  
  • Understanding of NLP algorithms and techniques 
  • Experience with Large Language Models (fine tuning, RAG, agents) 

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