About Pole Star: As the leader in maritime intelligence, Pole Star empowers better decision-making and protects clients’ business interests, assets, seafarers, vessels, cargo, infrastructure, investments, profitability, and reputation - through provision of high-performance, cyber-secure solutions underpinned by immense service and constant technological innovation. We have offices in London, USA, Singapore, Hong Kong and Panama, alongside presence in Australia. Teams are made up of over 19 nationalities, speaking 25 different languages. The Opportunity: As a Data Scientist, you will build advanced analytical, machine learning, and agentic AI solutions that power Pole Star’s maritime intelligence products and internal decision-making. You will work with rich time-series and geospatial datasets, including vessel positional data (AIS, RF and other sources), geospatial zones, and weather/ocean models, helping to turn these into high-value derived datasets and signals. You will contribute to Pole Star’s AI/ML roadmap: identifying high-impact use cases, experimenting with models (including LLMs and agentic AI), and working with Data Engineers and Product teams to move successful solutions into production in AWS. Responsibilities: Collaborate on Pole Star’s AI/ML roadmap and strategy, aligning data science work with product and business priorities. Develop and deploy machine learning and agentic AI use cases on maritime datasets (AIS, RF, geospatial, NWP, product/operational data). Perform feature engineering and model experimentation using Python and (where appropriate) Spark on complex time-series and geospatial data. Work with Data Engineers to productionise models and data products in AWS (batch and, where relevant, streaming/near real-time). Support the design and validation of data quality and reconciliation logic (e.g. AIS vs RF vs other positional sources), including quality scores and anomaly indicators. Validate, monitor, and improve model performance, setting up appropriate evaluation metrics and monitoring. Analyse and explain AI/ML solutions to technical and non-technical stakeholders, maintaining high ethical and governance standards. Document models, experiments, and lessons learned in code repositories and internal knowledge bases. Support analytics and BI teams with advanced modelling and statistical analysis for key business questions. Required Skills: 4 years of experience in a Data Scientist role, ideally in maritime, logistics, transportation or other complex time-series/geospatial domains. Strong Python programming skills for data analysis and modelling. Strong SQL skills and experience with analytical data stores. Experience with core ML libraries/frameworks (e.g. scikit-learn, TensorFlow and/or PyTorch, pandas, NumPy, SciPy). Experience working with time-series and/or geospatial data. Experience working in AWS environments (e.g. S3, Glue, EMR/Databricks, SageMaker or similar). Experience with notebook-driven development (Jupyter, VS Code, or similar) and Git-based workflows. Solid foundation in statistics and machine learning, including model evaluation and validation. Strong communication skills to present findings and explain model behaviour to technical and non-technical stakeholders. Additional Skills: (Nice to have skills which are not mandatory) Experience with LLMs, NLP and/or agentic AI solutions (e.g. Hugging Face or similar ecosystems). Experience with Spark or PySpark for large-scale data processing. Exposure to BI platforms (QuickSight, Tableau or similar). Experience with ML/AI Ops practices (model lifecycle, CI/CD for ML, monitoring). Experience with maritime intelligence, AIS data, RF signal data or NWP/metocean data. Experience with real-time analytics platforms (e.g. Tinybird or similar). Experience with additional languages (e.g. Scala, R, Java, C++), where relevant. Education/Certifications: Bachelor’s degree in Computer Science, Computer Engineering, Statistics, Mathematics, Electronics & Communications Engineering, or a related field; OR Master’s degree in Data Science, Artificial Intelligence, or a related discipline. Certifications in public cloud services related to Machine Learning / Data Science (AWS preferred) are an advantage. Employee Benefits: Hybrid/Flexible working Flexible Benefits Package Including: Private healthcare. Dental, Optical Salary sacrifice schemes Gym and wellness programs Childcare vouchers Buy/Sell holidays Cycle to work scheme Medicash health cash plan Home and tech scheme Life insurance, company funded to 3x salary Employee assistance program 25 days annual leave 5 wellbeing days Volunteering leave (2 days annually) Up to a 5% matching pension Unlimited learning and development opportunities Refer-a-friend recruitment bonus