Senior Business Analyst - Operations and Technology

Medefer is an innovative CQC registered healthcare provider that aims to transform the way that healthcare is delivered by enabling healthcare systems to provide patients with the best healthcare experience - timely, efficient and effective.

Founded by Consultants and GPs working in the NHS, our culture is driven by a deeply held commitment to the NHS and delivering transformational change to the patient experience. We are a passionate, dedicated and patient-focused team striving to make healthcare fit for the 21st century. Our vision is to completely re-frame the way that healthcare is delivered.

Medefer’s technology platform delivers world-class leading safety, whilst delivering a unique approach in delivering a national service to patients, from referral, to treatment and discharge.

Medefer has been at the forefront of digital transformation in NHS outpatient care for over a decade. We pioneered the consultant-led virtual hospital model, and our approach has been explicitly recognised by the UK government as one of three “tried-and-tested” innovations forming the blueprint for NHS Online – the national virtual hospital service launching in 2027.

That recognition reflects what we’ve already built. We are scaling into new ICBs and specialties, developing innovative pathways, and shaping how virtual care is delivered nationally. To do that well, we need people who can work across the full breadth of the organisation – Operations, Clinical, Product, and Engineering – to design, improve, and deliver the pathways and platform capabilities that will define the next generation of NHS virtual care.

This is a senior role for an experienced analyst who can work with a high degree of autonomy across the full breadth of the organisation – bringing structured diagnostic thinking, deep knowledge of delivery, and the confidence to lead complex workstreams from problem identification through to implemented solution. You will shape how we understand and improve our pathways and platform, not just execute within a defined brief.

What success looks like

• Problems are thoroughly understood before solutions are committed to

• Technology and operational changes are well-targeted and durable

• Temporary workarounds are tracked, managed, and resolved deliberately

• Requirements are clear enough for engineering to build from with confidence

• Changes are embedded across teams through effective training, SOPs, and communications

• Knowledge of how our platform and pathways work is documented and shared, not held by individuals
  

Understand problems thoroughly before proposing solutions

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  Draw on your experience to build a rapid, deep understanding of how clinical, administrative, and operational teams work – diagnosing where friction or risk exists and articulating the impact on patients, staff, and partners with clarity and evidence.

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  Produce concise problem statements that include: what the issue is, the evidence, the likely root cause, and a recommendation on how to address it – whether that’s a process change, training, a system fix, or a combination.

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  Use structured analysis methods (e.g. root cause analysis, process mapping, Lean waste identification) to get beyond surface symptoms.

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  When issues repeat or cluster, pull them together with a shared root cause hypothesis and a recommendation on how to address them as a whole – so we solve the underlying problem, not each symptom separately.

Drive operational improvement

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  Work with Operations and Clinical teams to design, document, and improve clinical and administrative pathways so they are safe, scalable, and clearly understood.

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  Identify where current SOPs are not being followed and work with teams to understand why – distinguishing between training gaps, unclear processes, and genuine process problems.

Translate needs into clear requirements (when technology change is the right answer)

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  Write clear user stories and requirements with acceptance criteria, edge cases, and test scenarios so engineering can build safely and predictably.

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  Work with Engineering to understand constraints, dependencies, risks, and sequencing.

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  Ensure appropriate clinical safety and information governance stakeholders are engaged for changes that affect clinical workflow, patient communications, reporting logic, or integrations, and that required evidence is captured before go-live.

Own delivery of your workstreams

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  Take ownership of getting work from problem identification through to implemented solution – whether that’s a process change, a system change, or both.

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  Coordinate across teams (Operations, Clinical, Product, Engineering, external partners) to keep work moving and resolve dependencies.

Essential

• Genuine curiosity and a growth mindset – a habit of asking “why”, exploring alternatives, testing assumptions with evidence, and actively seeking out new tools and ways of working to improve how you and your team operate.

• A minimum of five years’ proven experience as a business analyst, ideally in a regulated or safety-critical environment; healthcare experience is strongly preferred.

• A demonstrated track record of independently diagnosing complex operational problems using evidence and structured analysis – with the confidence to reframe or push back on requests when the underlying problem is not yet well understood.

• Strong process mapping and redesign skills – able to document complex pathways clearly and identify practical improvements.

• Ability to write clear, unambiguous requirements and acceptance criteria that engineering teams can build from.

• Good understanding of the software development lifecycle and how engineering teams work.

• Strong stakeholder engagement skills – comfortable working with clinicians, operations staff, engineers, and senior leadership.
  

Desirable:

• Exposure to formal improvement methodologies such as Lean, Six Sigma, or similar, applied in a real delivery context.

• Experience recognising patterns across individual issues and bundling them into coherent improvement plans.

• Practical experience using AI/LLMs in a work context (prompting, evaluating outputs, integrating into workflows).

• Experience with Lean, Six Sigma, or similar improvement methodologies.