Data Scientist & Engineer

Gail's Limited
London
2 days ago
Create job alert
Responsibilities
  • Develop advanced analytics / data science solutions to solve problems focused on forecasting, new site selection, ordering, production, rota scheduling, logistics and online services optimisation.
  • Extend functionality of our Bread GPT service (Large Language Model insight synthesis engine).
  • Data engineering: build and develop ETL processes in Microsoft Fabric to support reporting, insight and applied AI models.
  • A hands-on role working with other staff and partners.
  • Utilize data science and analytics to enhance application functionality and performance. Work with the data team to create and deploy machine learning models and AI-driven solutions for real-world applications.
  • Ensure the continuous development and delivery of solutions.
  • Monitor and evolve solutions.
  • Mentor and guide junior team members, fostering a culture of continuous learning and improvement.
  • Develop effective working relationships with colleagues within and beyond the Technology team to ensure that a consistent, high-quality service is delivered.
Required Qualifications
  • Ideally a bachelor’s degree in Computer Science, Analytics, Engineering, or a related field.
  • Minimum of 3+ years of experience with excellent knowledge of Python and preferably R.
  • Knowledge of ETL processes - ideally basic understanding of Microsoft ETL (Data Factory / Synapse / Fabric).
  • Knowledge of databases (SQL & NoSQL) and API development/integration.
  • Understanding of software development and application design.
  • Proven experience in building data science solutions and developing customised LLM applications.
  • Strong interest in technology.
  • Excellent problem-solving skills and attention to detail.
  • Knowledge of effective business analysis - ability to gather, document, and analyze business requirements effectively and the experience creating user stories, process flows, and wireframes.
  • Ability to work effectively in a fast-paced, dynamic environment.
  • Strong communication and collaboration skills.
  • "Can do" outlook and approach to work.
  • Demonstrate the ability to think around issues and look at the bigger picture to provide solutions through a variety of problem-solving techniques.
  • Ability to prioritise issues according to business needs, and to escalate when necessary/appropriate, and problem solve.
Preferred Qualifications
  • Experience in manufacturing, retail or hospitality industries.
  • Knowledge of programming languages and frameworks.
  • Free food and drink when working
  • 50% off food and drink when not working
  • 33 days holiday
  • Pension Scheme
  • Discounts and Savings from high-street retailers and restaurants
  • 24 hour GP service
  • Cycle to work scheme
  • Twice yearly pay review
  • Development programmes for you to RISE with GAIL's


#J-18808-Ljbffr

Related Jobs

View all jobs

Data Scientist & Engineer: Forecasting & LLM Apps

ML-Driven Data Scientist & Engineer (Azure)

Data Scientist/AI Engineer

Data Scientist/AI Engineer

Data Scientist/ Software Engineer

Data Scientist & Software Engineer (ML/Ops)

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.

AI Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation. This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think). If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.

How to Write an AI Job Ad That Attracts the Right People

Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace. Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality. In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert. Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility. This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.

Maths for AI Jobs: The Only Topics You Actually Need (& How to Learn Them)

If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day. The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics: Linear algebra essentials Probability & statistics for uncertainty & evaluation Calculus essentials for gradients & backprop Optimisation basics for training & tuning A small amount of discrete maths for practical reasoning This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.