Product Manager - Data infrastructure & Analytics

CM Medical Recruitment
Sheffield
1 year ago
Applications closed

Related Jobs

View all jobs

Data Analyst - Product

Applied Data Scientist

Senior Data Engineer

Lead Data Engineer

Data Science Manager – Property Tech – London

Data Science Manager - Property Tech - London

CM Medical is exclusively partnered with a leading provider of medical imaging AI solutions. Our partner offers healthcare customers the broadest portfolio of imaging AI solutions via a tried-and-tested platform, seamlessly integrated with customer systems. This platform simplifies the implementation and management of imaging and operational AI applications, enabling efficient addition of new applications to reduce implementation time, costs, and long-term maintenance efforts.


As the Product Manager for Data Infrastructure & Analytics, you will play a pivotal role in supporting the launch of new customer capabilities by leading the development and implementation of data storage, processing, querying, and visualization infrastructure for AI data and analytics across the platform. Your focus will be on transforming vast datasets into actionable insights, which help improve clinical workflows, patient outcomes, and drive AI adoption in healthcare.


Key Responsibilities:


  • Develop & Maintain Data Infrastructure Requirements:Ensure the infrastructure supports AI data storage, processing, querying, and visualization to meet customer and business needs.
  • Drive the Product Roadmap:Contribute to and execute on the product roadmap for data and infrastructure products, aligning with broader strategy and goals. Look for opportunities to deliver incremental value quickly by scoping initiatives efficiently.
  • Collaborate Across Teams:Work closely with stakeholders, including healthcare providers, platform partners, clinicians, data scientists, and software developers, to design and implement scalable solutions that meet technical and regulatory requirements.
  • Manage Product Development:Lead the product development lifecycle from concept to launch, ensuring that performance, scalability, and security standards are met.
  • Ensure Balanced Prioritization:Make well-balanced prioritization decisions, considering multiple perspectives and data points, to ensure product success and alignment with business needs.
  • Track Product Performance:Use analytics and metrics to track product performance, identifying opportunities for improvement and enhancement of data infrastructure and analytics capabilities.
  • Compliance & Security:Maintain awareness of best practices for data security in healthcare and comply with information security requirements outlined in the company’s security manuals.
  • Customer Engagement:Conduct customer interviews, gather feedback, and adjust the product strategy based on clinical workflows and user experiences to ensure alignment with customer needs.


Key Requirements:


  • Technical Expertise in Data Infrastructure:Hands-on experience with data infrastructure technologies, including storage, processing, querying, and visualization tools.
  • Knowledge of Database Systems & Data Models:Strong understanding of database systems, data models (e.g., IHE SOLE, OMOP), and industry standards for healthcare.
  • Proven Product Management Experience:Experience in healthcare technology, clinical informatics, or medical imaging informatics, with demonstrated proficiency in roadmap development, stakeholder management, and product lifecycle management.
  • Proficiency in Data Analytics Tools:Familiarity with data analytics tools and visualization platforms to support decision-making and product performance tracking.
  • Customer-Centric Focus:A strong commitment to engaging with customers, including clinicians and healthcare providers, to refine product strategies based on real-world usage and feedback.
  • Compliance & Regulatory Knowledge:Experience working with healthcare regulatory bodies and understanding industry standards and security requirements for healthcare applications.


Qualifications:


  • Bachelor’s degree in a relevant field (e.g., Computer Science, Engineering, Healthcare Informatics) or equivalent practical experience.
  • Experience in product management, especially in healthcare technology, medical imaging, or clinical informatics.
  • Strong communication skills with the ability to engage and influence a variety of stakeholders.

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.