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- Tasks: Build advanced AI solutions for maritime intelligence using rich datasets.
- Company: Pole Star, a leader in maritime intelligence with a diverse global team.
- Benefits: Flexible working, private healthcare, gym programs, and unlimited learning opportunities.
- Other info: Join a dynamic team with a focus on innovation and ethical AI practices.
- Why this job: Make an impact in maritime technology while working with cutting-edge AI and data science.
- Qualifications: 4+ years in data science, strong Python and SQL skills, experience with AWS.
The predicted salary is between 60000 - 80000 ÂŁ per year.
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 high‑performance, cyber‑secure solutions supported by continuous 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):
- 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
Data Scientist - Maritime Technology employer: Pole Star Global
Contact Detail:
Pole Star Global Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Scientist - Maritime Technology
✨Tip Number 1
Network like a pro! Reach out to people in the maritime tech space on LinkedIn or at industry events. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data science projects, especially those involving time-series and geospatial data. This gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for interviews by brushing up on your Python and machine learning concepts. Be ready to discuss how you've tackled real-world problems with data – they love hearing about practical applications!
✨Tip Number 4
Don't forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search.
We think you need these skills to ace Data Scientist - Maritime Technology
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the Data Scientist role at Pole Star. Highlight your experience with time-series and geospatial data, and don’t forget to showcase your Python and SQL skills. We want to see how your background aligns with our maritime technology focus!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about maritime intelligence and how your skills can contribute to our AI/ML roadmap. Keep it engaging and relevant to the job description – we love a good story!
Showcase Your Projects: If you've worked on any cool projects involving machine learning or data analysis, make sure to mention them! Include links to your GitHub or any relevant portfolios. We’re keen to see your hands-on experience and how you tackle real-world problems.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it gives you a chance to explore more about what we do at Pole Star!
How to prepare for a job interview at Pole Star Global
✨Know Your Data
Make sure you’re well-versed in the types of data you'll be working with, especially time-series and geospatial datasets. Brush up on your experience with vessel positional data and be ready to discuss how you've used Python and SQL to analyse similar datasets.
✨Showcase Your ML Skills
Prepare to talk about your experience with machine learning frameworks like scikit-learn or TensorFlow. Have examples ready that demonstrate your ability to develop and deploy models, particularly in AWS environments, as this is crucial for the role.
✨Communicate Clearly
Since you'll need to explain complex AI/ML solutions to both technical and non-technical stakeholders, practice simplifying your explanations. Think of ways to present your findings clearly and concisely, perhaps using visuals or analogies.
✨Align with Their Vision
Research Pole Star’s AI/ML roadmap and understand their business priorities. Be prepared to discuss how your skills and experiences align with their goals, and think of high-impact use cases you could contribute to right from the start.