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

Apply Now

Data & Insights Manager

Redruth
1 year ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager [UK]

Data Engineering Manager [UK]

Junior Data Analyst

Data Analyst – Senior Consultant, Assistant Manager, Manager - Advisory Consulting

Data Scientist

Audit Manager - Data Science. R00AOR05263

Data Insights Manager

Our client is transitioning from a traditional business model to a data-driven approach, focusing on maximising the value of their extensive data assets. Backed by venture capital for the past two years, they are working towards creating and monetising a subscription-based data product. To achieve this, they are building a solid data function that can generate valuable insights, guide product development, and drive commercial growth.
As part of this transition, they are seeking a strategic Data Insights Manager who will ensure the quality and accuracy of the company's data while working closely with stakeholders to translate insights into actionable business outcomes. You will play an integral role in supporting the development of data products that will enable the company to monetise its data effectively.

As the Data Insights Manager, you will lead a small data team, ensuring that all data is validated, accurate, and aligned with market needs. You will engage with both internal and external stakeholders to deliver valuable insights that support the company's commercial and strategic objectives. While this role is primarily focused on data insights, your work will contribute directly to the creation and refinement of the company's data products, helping to transform data into a marketable subscription-based offering.

Key Responsibilities

  • Stakeholder Engagement

    o Collaborate with internal teams, clients, and external industry experts to understand data requirements and refine data to meet specific needs.
    o Act as a primary contact for stakeholders, ensuring data insights align with business challenges and incorporating feedback into data processes.
    o Present insights to both technical and non-technical audiences, transforming complex data into clear, actionable outcomes that influence decision-making.

  • Data Validation and Governance

    o Lead efforts to implement robust data validation and governance frameworks, ensuring the accuracy, reliability, and integrity of the company's data.
    o Maintain high standards of data quality, ensuring that insights are fully validated and compliant with internal and external standards.
    o Continuously enhance the efficiency and accuracy of data processing and reporting mechanisms, ensuring data quality remains a priority.

  • Insight Generation and Reporting

    o Translate complex datasets into actionable insights, creating user-friendly reports and dashboards aligned with business objectives.
    o Work closely with commercial and product teams to enhance data offerings by identifying key trends and emerging opportunities within the data.
    o Communicate findings clearly through reports and presentations, helping stakeholders understand the strategic value of data insights.

  • Involvement in Data Product Development

    o Support the creation of client-facing data products, offering insights that guide product development, packaging, and delivery.
    o Collaborate with leadership to shape the strategic direction of data products, ensuring they meet client needs and are designed for commercial success.
    o Play a key role in positioning data as a core product offering, contributing to the company's overall data-driven vision.

  • Innovation and Tool Selection

    o Identify and implement innovative tools, technologies, and processes that enhance the company's ability to deliver high-quality data insights and products.
    o Stay informed of emerging trends in data analytics and data science, ensuring the company adopts the latest technologies.
    o Recommend investments in tools and resources to further the company's data capabilities, collaborating with senior leadership to secure these investments.

    Technical Skillset

  • Strong experience with data visualisation tools such as Power BI, Tableau, or similar platforms, with the ability to create compelling, data-driven stories.
  • Expertise in SQL and data processing, ensuring data accuracy and quality at all stages of the analytics process.
  • Experience with programming languages like Python or R for advanced analytics, modelling, and data manipulation.
  • Familiarity with data governance frameworks, including the ability to implement and maintain high standards of data validation and integrity.
  • Knowledge of cloud-based data platforms (Azure, AWS, or Google Cloud) and experience integrating data from multiple sources.

    Candidate Profile

  • Demonstrates exceptional stakeholder engagement skills, with experience tailoring data insights to business needs.
  • Proactive, innovative thinker with a deep understanding of modern data analytics tools and techniques.
  • Collaborative, with the ability to work cross-functionally and influence key stakeholders in both technical and non-technical environments.
  • Strategic, with a focus on driving data products that support business growth and client value.

    Salary and Benefits

  • Performance-based bonus scheme
  • Pension contributions
  • Private healthcare
  • 25 days of annual leave
  • Hybrid working arrangement (2-3 days per week in the office)
  • Investment in tools and technologies to support data initiatives

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.

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

Summary: UK machine learning hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise shipped ML/LLM features, robust evaluation, observability, safety/governance, cost control and measurable business impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for ML engineers, applied scientists, LLM application engineers, ML platform/MLOps engineers and AI product managers. Who this is for: ML engineers, applied ML/LLM engineers, LLM/retrieval engineers, ML platform/MLOps/SRE, data scientists transitioning to production ML, AI product managers & tech‑lead candidates targeting roles in the UK.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.