Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Solution Architect - Advisory, Insights

Austin Fraser
Glasgow
9 months ago
Applications closed

Related Jobs

View all jobs

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Senior Solutions Architect (Big Data) – Outside IR35

Solution Architect - Advisory, Insights

Salary:£100,000 - £120,000 - Bonus + Pension + Private Healthcare

Location:London / UK Wide Location - Hybrid working

* To be successfully appointed to this role, you must be eligible forSecurity Check (SC) clearance.

The Client:

83zero is proud to be partnered with a global leader in digital services, driving innovation in customer experience through CRM, marketing, business intelligence, and cloud solutions. Their cutting-edge technologies are tailored for enterprise clients, delivering platforms that not only meet today's business needs but also pave the way for future growth. These solutions empower digital transformation initiatives, unlock new business opportunities, and make customer relationship operations more relevant in today's evolving landscape.

Hybrid Working:Your work locations will vary based on your role, business needs, and personal preferences. This will include a mix of office-based work, client sites, and home working, with the understanding that 100% home working is not an option.

Your Role:

  • Skilled Architects who bring a blend of consulting skills, with data and insights experience.
  • You will be able to lead teams of talented colleagues across architecture, insights and data to transform the way companies and government operate.
  • Our team is on a growth trajectory and we are looking for someone to help to accelerate this growth.

Your Skills and Experience:

  • Provide clearly articulated points of view on topics of focus, such as AI platforms, data engineering, security and privacy, DataOps, migration strategies etc.
  • Be a lead for fresh engagements, forming excellent relationships with client teams and building bridges for delivery activities.
  • Forge excellent links with related disciplines across the organisation, including AI engineering, cloud infrastructure, customer software development, consulting, systems engineering etc. and forge excellent links with partners and vendors across the industry to ensure that they always provide a leading point of view.

Experience:

  • Advisory skillsets including consulting, influencing, communication, coaching and mentoring skills.
  • Strong track record of architecting, designing and delivering complex large-scale data and/or analytics and AI centric solutions.
  • Experience of architecting solutions deployed in cloud, on-prem and hybrid or multi-cloud environments.

To apply please click the "Apply" button and follow the instructions.

For a further discussion, please contactCaitlin Earnshawon#removed#or alternatively email:

#J-18808-Ljbffr

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.

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.