Data Solution Designer Data Science

Stackstudio Digital Ltd.
King's Lynn
22 hours ago
Create job alert

Role / Job Title:Data Solution Designer Data ScienceWork Location:Norwich 3 Days (Flexible)Duration of Assignment:06 MonthsThe RoleThe Data Solution Designer Data Science is responsible for designing end to end data science and advanced analytics solutions that translate complex business problems into scalable, secure, and high performance data products.This role bridges business stakeholders, data engineering, data science, and IT architecture teams, ensuring solutions are production ready and aligned with enterprise standards.Your ResponsibilitiesSolution & Data Model Design1. Solution Design & Architecture

  • Design end to end data science solutions including data ingestion, feature engineering, model development, deployment, and monitoring
  • Define logical and physical architectures for analytics platforms, ML pipelines, and AI products
  • Ensure solutions are scalable, reusable, secure, and cost effective
  • Select appropriate ML/AI techniques (e.g., regression, classification, NLP, forecasting, clustering)

2. Data & Analytics Engineering Alignment

  • Work closely with data engineers to define:
    • Data models and schemas
    • Data quality rules
    • ETL / ELT pipelines
  • Define feature stores, training datasets, and inference pipelines

3. Model Development & Deployment Strategy

  • Guide data scientists on:
    • Model selection and evaluation strategies
    • Experiment tracking and reproducibility
  • Design MLOps frameworks for:
    • CI/CD of ML models
    • Model versioning and governance
    • Monitoring drift, accuracy, and bias

4. Technology & Platform Governance

  • Define standards for:
    • Programming languages and frameworks
    • Cloud vs on prem deployments
    • Security, privacy, and compliance
  • Ensure adherence to data governance, regulatory, and risk controls (especially in BFSI)

5. Documentation & Best Practices

  • Produce:
    • High level architecture diagrams
    • Low level design documents
    • Non functional requirement specifications
  • Establish best practices and reusable design patterns

Your ProfileEssential Skills / Knowledge / ExperienceData Science & ML

  • Supervised and unsupervised learning
  • Time series, NLP, recommendation systems (as applicable)

Programming

  • Python (NumPy, Pandas, Scikit learn)
  • Optional: R, SQL

Data Platforms

  • Relational & NoSQL databases
  • Big data frameworks (Spark, Hive, Databricks)

MLOps & Deployment

  • Model lifecycle management
  • CI/CD pipelines
  • Containerization (Docker, Kubernetes desirable)
  • Model packaging and REST APIs

Cloud & Tools (Any combination)

  • AWS / Azure / GCP analytics and ML services
  • MLflow, Azure ML, SageMaker, Vertex AI
  • Version control (Git)

Domain & Soft Skills

  • Strong analytical and problem solving skills
  • Ability to explain complex data science concepts in simple business language
  • Experience working in Agile / Scrum environments
  • Stakeholder management and decision facilitation

Preferred Qualifications

  • BFSI domain experience (risk, fraud, AML, credit, customer analytics)
  • Experience with regulatory data modelling and explainable AI (XAI)
  • Exposure to GenAI, LLMs, and vector databases

Desirable Skills / Knowledge / Experience

  • TOGAF or cloud architecture certifications


JBRP1_UKTJ

Related Jobs

View all jobs

Data Solution Designer Data Science

Data Solution Designer Data Science

Data Solution Designer Data Science

Machine Learning Manager, Munich

Data Scientist

Associate Director, Data Science/Gen AI Lead - ER&I

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 AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.