Principal Data Science Consultant

Harvey Nash
London
4 days ago
Create job alert

Partnered with a Global Specialist Cloud Consultancy working with various household brands who are on the ramp up to upscale their Data Science capabilities and looking to build out top tier resources in this area of speciality in a Senior and Principal capacity.
Role Requirements:

Develop and deploy Machine Learning models on Google Cloud.
Collaborate with various clients to understand their business challenges and design technical solutions utilising Machine Learning models.
Strong understanding of Machine Learning algorithms for supervised and unsupervised learning.
Collaborate across the various client base utilising and developing AI agents, Cloud ML tools, MLOps and Python.

Skills/Experience:

Excellent communication and strong level of consulting/client facing experience.
Comprehensive understanding of Data landscape and proactive nature in staying up to date with latest market trends.
Business focus and outcome oriented.
Capable of working independently and as part of a team setting.

If this role aligns with your career aspirations and you’d like to know more please share your CV and availability for a call to
#J-18808-Ljbffr

Related Jobs

View all jobs

Principal Data Science Consultant

Principal Data Science & AI Consultant — Clinical Analytics

Principal Data Strategy & AI Advisory Leader

Principal Data Architect

Senior Data Engineer

Mid/Senior/Principial Data Engineers - Multiple hires.

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.

New Data Science Employers to Watch in 2026: UK and International Companies Leading Analytics and AI Innovation

Data science has emerged as one of the most transformative forces across industries, turning raw information into actionable insights, predictive models, and AI-powered solutions. In 2026, the UK is witnessing a surge in organisations where data science is not just a support function but the core of their products and services. For professionals exploring opportunities on www.DataScience-Jobs.co.uk , identifying these employers early can provide a competitive advantage in a market with high demand for advanced analytics and machine learning expertise. This article highlights new and high-growth data science employers to watch in 2026, focusing on UK startups, scale-ups, and global firms expanding their data science operations locally. All of the companies included have recently raised investment, won high-profile contracts, or significantly scaled their analytics teams.

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.

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.