Data Scientist - Customer Data

West End
3 months ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Data Scientist

Data Scientist

Data Scientist

Data Scientist (Government)

Data Scientist (NLP & LLM Specialist)

Data Scientist - Customer Data
Salaries in the region of £65,000 - £75,000 DoE
Hybrid working - 2 days central London office
Job Reference J13031

Full UK working rights required/no sponsorship available

Immediate requirement - strong leadership skills

We are seeking for an experienced, passionate and highly motivated Data Scientist who will help discover the information hidden in vast amounts of customer data, and help make data driven decisions to deliver better products, service and relevance to the customers.

THE ROLE
Customer Science
• Develop and implement predictive models to understand drivers of customer behaviour, including purchase patterns, customer lifetime events and sentiment analysis
• Create sophisticated customer segmentation using behavioural, transactional, and demographic data
• Design and build predictive models to enhance personalized customer experiences across all channels
• Collaborate on design of test & learn methods to measure CRM initiatives' effectiveness
• Monitor and optimize model performance through continuous improvement cycles

Technical Implementation
• Transform analytical solutions into production-ready code
• Implement models within our existing technology stack
• Ensure scalability and efficiency of deployed solutions

Stakeholder Communication & Collaboration
• Translate complex analytical findings into clear, actionable insights
• Create compelling data visualizations to effectively communicate patterns and insights
• Partner with cross-functional teams to enhance CRM strategies
• Provide data-driven recommendations to improve customer engagement metrics

Skills
● Relevant experience in Customer Marketing Data Science including applied statistics and machine learning techniques (supervised and unsupervised learning, natural language processing, Bayesian statistics, time-series forecasting, collaborative filtering etc)
● Proficiency in Python with familiarity to ML libraries e.g. pandas, numpy, scipy, scikit-learn, tensorflow, pytorch)
● Familiarity with cloud platforms (GCP, AWS, Azure) and tools like Dataiku, Databricks.
● Experience with ML Ops, including model deployment, monitoring, and retraining pipelines.
● Ability to work cross-functionally with marketing, CRM, and engineering teams.
● Excellent communication skills
● Experience in a global or multi-regional context is a plus

If you would like to hear more, please do get in touch.

Alternatively, you can refer a friend or colleague by taking part in our fantastic referral schemes! If you have a friend or colleague who would be interested in this role, please refer them to us. For each relevant candidate that you introduce to us (there is no limit) and we place, you will be entitled to our general gift/voucher scheme.

Datatech is one of the UK's leading recruitment agencies in the field of analytics and host of the critically acclaimed event, Women in Data. For more information, visit our website: (url removed)

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.

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.

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.

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.