Senior Data Scientist - AI Practice Team

American Bureau of Shipping
Warrington
1 month ago
Create job alert

We are seeking an exceptional Senior Data Scientist to join us full-time in our Artificial Intelligence (AI) Practice Team, Europe. In this role, you will lead the design and delivery of analytics and machine learning solutions across policy, data, and document-centric AI engagements, working with complex real-world datasets from industrial and asset-intensive domains. You will partner closely with consultants, domain experts, and junior data scientists to turn client data into robust models, reusable assets, and decision-ready insights. Based in Warrington or London England with some remote flexibility, you will help shape our technical approaches, uplift data science practices, and ensure solutions are production-aware and business-relevant.

What You Will Do: 

Lead the preparation, exploration, and analysis of client data (tabular, time-series, and document-based) to enable robust modeling, feature engineering, and insight generation. Design, implement, and validate machine learning models and analytics pipelines, including problem framing, model selection, evaluation, and iteration for real-world performance. Drive advanced use of NLP and document understanding techniques to extract, transform, and enrich information from reports, PDFs, logs, and other unstructured sources. Build and maintain clear, impactful dashboards, reports, and visualizations (, in Python, Power BI, or similar tools) to communicate findings to consultants and client stakeholders. Collaborate with consultants and domain experts to translate business problems into analytical solutions, articulate trade-offs, and present recommendations to technical and non-technical audiences. Ensure technical quality, reproducibility, and governance by establishing good practices for code, documentation, data management, and model tracking across projects. Mentor and support junior data scientists, providing guidance on methods, tooling, and best practices, and reviewing their work for quality and consistency.

What You Will Need:

Education and Experience 

Bachelor’s degree in a STEM discipline (, Data Science, Computer Science, Engineering, Mathematics, Statistics) or related field; Master’s degree preferred or equivalent experience. + years of experience applying data science and machine learning in professional settings, including end-to-end delivery of analytics/ML solutions. Proven track record working with real-world, messy datasets (including unstructured/document data) across the full lifecycle: data preparation, modeling, evaluation, and deployment handoff. Experience leading or owning significant workstreams within AI/ML or analytics projects, ideally in consulting, industrial, or asset-intensive environments. Practical experience working with cloud-based and modern data platforms (, Azure, AWS, GCP, Databricks) and integrating with enterprise data sources and workflows.

Knowledge, Skills, and Abilities 

Deep proficiency in Python for data science (pandas, scikit-learn, and related libraries) and strong SQL skills for working with relational and analytical data stores. Strong grounding in statistics, machine learning, and model evaluation, including supervised/unsupervised methods, feature engineering, and performance diagnostics. Hands-on experience with NLP and document understanding (, text preprocessing, embeddings, classification, information extraction, transformers/LLMs) applied to real datasets. Ability to design and implement robust, maintainable analytics and ML pipelines, using notebooks and production-ready code with Git-based version control. Familiarity with modern data and ML tooling (, Databricks, MLflow, Docker, CI/CD for data/ML) and good practices for experiment tracking and reproducibility. Proficiency with BI/visualization tools (, Power BI, Tableau) and data storytelling skills to communicate complex analytical results to non-technical stakeholders. Excellent communication and stakeholder engagement skills, with the ability to frame analytical approaches, explain trade-offs, and align solutions with business objectives. Proven ability to work across multiple projects, manage priorities, and operate in a fast-moving, consulting-style environment, while mentoring junior team members. Nice to have: exposure to industrial, maritime, or asset-intensive domains, or prior experience in AI consulting or client-facing roles. Must hold a valid right to work status in the UK.

Reporting Relationships

This role reports to the Project Manager and does not include direct reports.

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist and Machine Learning Researcher

Senior Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.