Ai & Data Engineer

BCN
Reading
2 months ago
Applications closed

Related Jobs

View all jobs

Data Engineering Product Owner, Technology, Data Bricks, Microsoft

Data Engineering Product Owner, Technology, Data Bricks, Microsoft Stack

Data Engineering Product Owner, Technology, Data Bricks, Microsoft

Data Engineering Product Owner, Technology, Data Bricks, Microsoft

Lead Data Engineer

Data Scientist

Job title: AI & Data Developer

Location: Hybrid (strong preference for Reading office)

Hours: Monday to Friday, 37.5 hours per week

Salary: Competitive + BCN benefits



About BCN

:At BCN we unite people and technology to enable organisations to fly


.
We believe people and organisations can achieve anything using technology to it’s full potential. Our role is to help them understand what is possible, implement in the right way and utilise their technology to achieve their ambitions. Which is why we put people front and centre – building client relationships for life and fostering a culture where our people thriv


e.
We are a leading managed IT services provider and technology consultant, specialising in delivering transformative technology solutions with industry-leading client experience across business, public sector and not for profit organisations. From cloud computing, cybersecurity, and data management to power app development, we are dedicated to pioneering technology with Microsoft innovati


on.
Guided by our 3 values of building relationships, customer success and passion and dedication, we are on a mission to make BCN the most trusted tech partner in the UK today. The kind of company clients want to work with, and people want to work


for.
We are delighted you are on this journey wit



h us!
Focus of th

e role:We are looking for a DP-100–certified Azure Data Scientist who is passionate about applied machine learning and delivering real improvements to our clients in smart, effective ways. The successful candidate will be eager to deepen their skills and broaden into wider Azure analytics capabilities, building a long career here at BCN Group within a growing and exciting innovation technolog


y team.
Our people have three things in common: a curiosity for learning, a drive to deliver projects brilliantly, and a belief that together we make a dif


ference.
Using your expertise in Azure Machine Learning, Python (pandas, scikit-learn) and Azure SDKs, you will help shape our Data & Productivity capability—designing, training, and deploying models with MLflow/AutoML, and integrating them into real business processes. You’ll collaborate across disciplines (Data Engineering, Security, Product) and work with services such as Azure Storage, Azure DevOps/GitHub Actions, and Fabric/Power BI to ensure solutions are observable, governed, and production-ready. This is an exciting opportunity to develop new capabilities and establish industry-leading machine-learning practices that drive measurable outcomes for our



clients.
Respon

  • sibilities:Work directly with business stakeholders to conceive, design and deliver end-to-end machine learning solutions, aligned to operational goals and governance
  • standards.Prepare and transform data for modelling using Python and Azure-native tools, ensuring quality and consistency across the ML and AI
  • lifecycles.Evaluate, and deploy models using AutoML, MLflow, and custom pipelines, with a focus on performance, scalability, and maint
  • ainability.Monitor model performance, detect drift, and implement retraining strategies to maintain relevance an
  • d accuracy.Apply responsible AI principles including fairness, explainability, and privacy to support ethical and transparent model d
  • evelopment.Collaborate with cross-functional teams to integrate ML and AI solutions into business processes, improving engagement, productivity, and decis
  • ion-making.Support our clients maintain and optimise Azure ML environments to deliver reproducible experimentation and efficient


deployment.
Person, Skills &

  • Experience:Certified in DP-100 Azure Data Scientist Associate
  • (mandatory).Confident working independently or collaboratively to support solution design, delivery, and governance across the ML and A
  • I lifecycle.Experienced in designing, training, registering, and deploying models using Azure Machine Learning (AML), AutoML,
  • and MLflow.Proficient in Python, pandas, and scikit-learn for data science and feature engineering, with a strong focus on rigorous validation and experime
  • nt tracking.Skilled in deploying models to real-time REST endpoints and orchestrating batch inferenc
  • e pipelines.Capable of implementing MLOps practices including CI/CD pipelines, model registry, environment management, and promotion across dev/test/
  • prod stages.Solid understanding of cloud and data engineering principles including storage, compute, and pipeline or
  • chestration.Strong ability to collaborate with subject matter experts to map current-state business processes and translate them into machine learning opportunities with measurable operati
  • onal impact.Clear communicator with excellent problem-solving skills, able to engage effectively with both technical and non-technical s
  • takeholders.Exposure to Kubernetes, Azure AI Search, or Azure AI Foundry to support scalable and efficient soluti
  • on delivery.Understanding of generative AI techniques and language model optimisation to enhance solution capability and
  • innovation.Familiarity with ethical AI frameworks and compliance standards to ensure responsible and transparent model
  • development.2+ years of experience in applied machine learning or data science roles, contributing to the delivery of impactfu
  • l solutions.Proven track record of deploying machine learning models into production environments, supporting business outcomes and operational



efficie

  • ncy.
    Why BCN?The opportunity to shape your own future with industry leading training and development, with access to ou
  • r BCN Academy.Competitive salary with the abilit
  • y to progress.23-days holiday allowance, increasing with length of service, plus bank holidays, an extra day off on your birthday and the optio
  • n to buy more!Company p
  • ension scheme.2 paid leave days per year to volunteer and support your local community – if it matters to you it
  • matters to us.Health cash plan with free access to a confidential Employee Assistance Programme (EAP) supporting bereavement, financial, health and wellbeing,
  • and much more
  • Life assuranceCycle to work scheme, electric vehicle scheme, home and tech scheme, and ret
  • ail discounts.Balancing work, life, and fitness can be challenging, so we offer a free on-site gym at our Manchester and Leeds locations to make it easier t
  • o stay active.Long service recognition to celebrate all
  • the milestonesBeer (or soft drinks) and Pizza Friday’s, dress down every day, social events such as Summer BBQ, Christmas party


and lots more!

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.

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.

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.