Data Scientist

Sagacity
London, United Kingdom
Today
£40,000 – £60,000 pa

Salary

£40,000 – £60,000 pa

Job Type
Permanent
Work Pattern
Full-time
Work Location
On-site
Seniority
Mid
Education
Degree
Posted
30 Apr 2026 (Today)

Purpose of the role

To support the design, prototype and delivery of innovative, data-led products by combining market insight, advanced analytics and modern data platforms. Working in closely with Product Managers and Data Analysts, this role will convert data assets and models into scalable, commercially viable analytical insights, data visualisations and product features.

The Product Innovation Analyst bridges commercial opportunity and technical feasibility, ensuring new products are designed with platform capabilities, data quality, performance and scalability in mind.

Principal responsibilities

  • Identify and define new data product opportunities enabled by advanced analytics, machine learning and large-scale data processing
  • Evaluate and test emerging AI technologies and analytical techniques and their suitability for use within our Customer Intelligence Platform to unlock new product capabilities
  • Drive product innovation from concept to launch, translating business and customer needs into technical product requirements and delivery specifications
  • Lead rapid prototyping and proof-of-concept development using Databricks notebooks, analytical outputs and machine learning techniques to validate product concepts
  • Own technical product definition, including data structures, feature sets, scoring methodologies, model architectures and delivery formats in collaboration with the Product Team
  • Define and monitor technical success metrics (data coverage, refresh latency, model stability) alongside commercial KPIs to optimise product performance

Product Innovation & Technical Design responsibilities:

  • Identify new product opportunities enabled by advanced analytics, machine learning and large-scale data processing
  • Identify new usage of existing attributes and products to create more value in existing data
  • Translate business and customer needs into technical product requirements
  • Support rapid prototyping and proof-of-concept development using Databricks notebooks and analytics outputs
  • Define product-level data structures, feature sets, scoring outputs and delivery formats in collaboration with the Product Team

Product Launch & Performance responsibilities:

  • Define technical success metrics (data coverage, refresh latency, model stability) alongside commercial KPIs
  • Support internal enablement by translating technical product detail into usable sales and client-facing materials
  • Drive continuous optimisation using usage analytics, customer feedback and platform performance insights

What success looks like in the role

  • Clear, concise and insightful data analytics which enable sound business decisions based on fact
  • Ability to translate data analysis into targeted information which can be converted into actionable improvements, based on specific client, sector, internal product need
  • Cross functional collaboration to enable continued improvement of Sagacity’s Product Suite through the delivery of robust data insights
  • Ability to take accountability and ownership for client and internal deliverables
  • Your efforts result in streamlined data analysis, product builds and reduced time to market

Competencies and Behaviours

  • 1 -3 years analytics / data science experience
  • Practical knowledge of; Delta Lake architecture and versioned datasets, Data pipelines, orchestration and scheduling concepts
  • Proficiency in analytical programming language such as python and/or SQL, with the ability to interrogate datasets and validate analytical outputs
  • Experience designing data products using large-scale transactional, behavioural or marketing datasets
  • Understanding of data modelling concepts (fact/dimension models, feature engineering, aggregations)
  • Can balance time across multiple projects. Plans ahead working backwards from deadlines with all necessary steps e.g. testing, QA. Proactively identifies risk and suggests mitigation
  • Is curious, sceptical, inquisitive, suggests ‘next steps’ analysis and translates analytical findings to actionable insight
  • Flexible, self-motivated, good under pressure, has a commitment to personal development
  • Excellent communication skills, both written and verbal, with a willingness to engage and influence others
  • Commercial experience within Telecoms, Banking or Utilities industries; or within a data related consultancy company would be beneficial
  • Able to travel throughout the UK
  • Can be based at our London Office (min 2 days per week on site)
  • Have the right to work in the UK

Related Jobs

View all jobs

Data Scientist

Adaptable Recruitment Liverpool, United Kingdom
£50,000 – £60,000 pa Hybrid

Data Scientist

Harnham - Data & Analytics Recruitment London, United Kingdom
£50,000 – £65,000 pa Hybrid

Data Scientist

Searchability NS&D Cheltenham, United Kingdom
£45,000 – £75,000 pa Permanent Clearance Required

Data Scientist

Franklin Bates London, United Kingdom
£55,000 – £65,000 pa Hybrid

Data Scientist

Hays Technology London, United Kingdom
£600 – £1,000 pd

Data Scientist

ISR Recruitment Exeter, Devon, United Kingdom
£50,000 – £60,000 pa Hybrid

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.

Where to Advertise Data Science Jobs in the UK (2026 Guide)

Advertising data science jobs in the UK requires a different approach to most technical hiring. Data science spans a broad and often misunderstood spectrum — from statistical modelling and experimental design through to machine learning engineering, product analytics and AI research. The strongest candidates identify firmly with specific subdisciplines and are frustrated by adverts that conflate data scientist with data analyst, business intelligence developer or machine learning engineer. General job boards produce high application volumes for data roles but consistently fail to match specialist data science profiles with the right opportunities. This guide, published by DataScienceJobs.co.uk, covers where to advertise data science roles in the UK in 2026, how the main platforms compare, what employers should expect to pay, and what the data says about hiring across different role types.

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.