Data Scientist, Senior Consultant, Digital Innovation

Deloitte LLP

City of London

4 months ago

Applications closed

Related Jobs

View all jobs

Data Scientist / AI Engineer

Senior Data Engineer (AWS, Airflow, Python)

Data Scientist, United Kingdom - BCG X

Senior Data Science Consultant

Managing Consultant, Customer Data Analytics / Data Science

Senior Data Scientist

Deloitte’s Tax Digital Innovation team is a key strategic function within the tax and legal business, driving exciting and innovative growth opportunities. You’ll work as part of a multi-disciplinary team of industry, tax and legal SMEs, technology, and data specialists to provide specialist data science expertise and support on a diverse range of projects with both an internal and client focus.

Your role will be as a Data Scientist within our expanding AI & Data Studio, working on new and existing projects and products, delivering insights to clients, and utilising data to make smarter, better decisions. You will apply data mining techniques, undertake statistical analysis, deploy cutting-edge machine learning tools and techniques, and draw insights and build predictive models on both structured and unstructured data.

This role would suit someone who has a passion for keeping up to date with the latest AI research and going from theory to production. You will thrive in a creative and collaborative environment, getting to create novel AI solutions to complex business problems.

Key Responsibilities:

Proactively source external structured and unstructured data sets to enhance services and insights
Process, cleanse, and verify the integrity of data used for analysis
Perform ad-hoc analysis and present results from a business-centric perspective
Build innovative models to classify and predict tax and legal data leveraging state-of-the-art machine learning and deep learning techniques
Design, build and test data pipelines for machine learning that are reliable, scalable, and easily deployable
Perform constant tracking of performances
Clearly and confidently articulate the value and benefits of delivering analytics projects to clients
Work closely with Tax and Legal SMEs to build a deep understanding of business/client challenges and in the development of Proof of Concepts
Share, communicate and develop bespoke AI solutions
Drive automation and optimisation of business workflows, helping clients to drive efficiency

Requirements:

Strong understanding of core machine learning algorithms and basic statistical methods
Experience experimenting with Generative AI (e.g. LangChain, Hugging Face, LlamaIndex)
Proficient in Python and key data science libraries (NumPy, Pandas, scikit-learn)
Experience working with structured and unstructured datasets
Familiar with version control (Git) and collaborative coding practices
Good communication and teamwork skills
Exposure to Azure data & AI services such as Azure Machine Learning, Foundry etc.
Basic knowledge of MLOps/DevOps concepts (pipelines, deployment, monitoring)
Understanding of model evaluation and reliability checks
Familiarity with frameworks like TensorFlow or PyTorch
Experience with data visualisation tools such as PowerBI and Tableau

We are an equal opportunities employer and welcome applications from all qualified candidates. We are committed to making an impact and creating an environment where you can experience a purpose you believe in, the freedom to be you, and the capacity to go further than ever before.

#J-18808-Ljbffr

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

Industry Insights

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

Feb 10, 2026

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Feb 3, 2026

Jobs

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Jan 23, 2026

Education

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.