Data & Insights Manager

Redruth
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst

Data Analyst - Drive Real Estate Insights

Data Analyst

Data Analyst

Data Analyst

Data Analyst

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.

Jobs

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

Education

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.