Specialisms

Product Management

Product Management is the driving force behind successful tech products. It’s a multifaceted role that bridges the gap between user needs, business objectives, and technological capabilities. Product managers are often described as the “CEO of the product,” responsible for its vision, strategy, and execution.

Roles we cover

  • Chief Product Officer

  • VP of Product

  • Product Director

  • Head of Product

  • Group Product Manager

  • Principal/Lead Product Manager

  • Senior Product Manager

  • Technical Product Manager

  • Data Product Manager

  • Product Manager

  • Product Owner

  • Associate Product Manager

    Key insights

    Product managers need a unique blend of business acumen, technical understanding, and user empathy

    The role is becoming increasingly data-driven, with decisions backed by user analytics and market trends

    As products become more complex, specialisations like Technical Product Manager and AI Product Manager are emerging

    Agile methodologies have significantly influenced product management practices, emphasising iterative development and continuous user feedback

    Software & Cloud Engineering

    Software and Cloud Engineering form the backbone of our digital world. These roles are responsible for designing, developing, and maintaining the software systems and cloud infrastructure that power modern businesses and technologies.

    Roles we cover

    • Chief Technology Officer

    • Engineering Managers & Directors

    • Backend Engineer

    • Frontend Engineer

    • Full Stack Engineer

    • Cloud Engineer

    • Data Engineer

    • Devops Engineer

    • QA & SDET (Software Development Engineer in Test)

    • iOS & Android Developer

    • Business Intelligence Engineers

    • Analytics Engineers

      Key insights

      The shift towards cloud computing has dramatically changed the landscape of software engineering, requiring new skills and approaches

      DevOps practices have blurred the lines between development and operations, promoting a culture of continuous integration and deployment

      Full-stack development has gained prominence, with engineers expected to work across the entire technology stack

      Mobile development remains crucial, with iOS and Android specialists in high demand as mobile usage continues to grow globally

      The rise of microservices architecture has led to increased complexity in system design, requiring specialised skills in distributed systems

      Machine Learning & AI

      Machine Learning and Artificial Intelligence represent the cutting edge of technology, pushing the boundaries of what’s possible in data analysis, automation, and intelligent systems.

      Roles we cover

      • Machine Learning Engineer

      • Data Scientist

      • AI Product Manager

      • Computer Vision Engineer

      • Natural Language Processing (NLP) Engineer

        These specialisms are interconnected, often working together to create innovative solutions. For instance, a product manager might work closely with software engineers and AI specialists to develop an intelligent, cloud-based application. As technology continues to advance, the boundaries between these fields may blur further, leading to new hybrid roles and specialisations.

        Key insights

        AI and ML are no longer confined to tech giants; companies across various industries are leveraging these technologies to gain competitive advantages

        The field is rapidly evolving, with new techniques and models emerging regularly, requiring professionals to engage in continuous learning

        Ethical considerations in AI, such as bias in algorithms and data privacy, are becoming increasingly important

        Specialised roles reflect the diverse applications of AI in various domains

        The integration of AI into products has created a demand for AI Product Managers who can bridge the gap between technical capabilities and business applications

        Data quality and availability remain critical challenges in AI and ML projects, emphasizing the importance of data engineering and data science roles