The role

Dyad is seeking a Data Scientist to help grow our analytical capabilities across our teams. This role fits someone who can pull, interrogate, and shape data from across the company, document the evaluations and benchmarks that matter to our AI Platform team and to Commercial, and turn all of it into dashboards, reports, and presentations that other people can act on.

The role priorities end of the data-science spectrum. It prioritises fluency with Python, SQL, and visualisation; clear reasoning about data quality and measurement; and communicating complex findings to stakeholders across the business. Communication fluency is a first-class requirement: a correct analysis that stakeholders cannot act on is a failure of the role, not of the audience.

You will work across Commercial, AI Platform, and BetterLetter, reporting into the Chief Clinical Product Officer.

This role is offered on a hybrid basis from our London office

Core responsibilities

Data extraction and analysis

• Work with BetterLetter, AI Platform, QARA, and Commercial to pull data from production systems, customer environments, and internal tooling.
• Clean, join, aggregate, and interrogate datasets with rigour in order to communicate findings to all stakeholders.
• Flag where data is missing, unreliable, or not yet instrumented to support the question being asked, and recommend what to do about it.

Dashboarding and reporting

• Build and maintain dashboards for internal teams (product, commercial, leadership) and, where appropriate, customers.
• Produce recurring reports (customer-facing metrics, operational KPIs, board packs and investor updates as that becomes necessary) that are accurate, legible, and consistent over time.
• Run bespoke analyses to support sales, renewals, clinical conversations, and strategic decisions.
• Present findings clearly to non-technical audiences, including senior leadership and customers.

Benchmarks and evaluations

• Turn benchmark and evaluation outputs produced by the AI Platform team into documentation, reports, and visualisations that other teams can use.
• Communicate technical evaluation metrics in understandable ways, and describe how evaluation results change over time in terms non-specialists can act on.

Requirements

Experience and background

A track record of applied data analysis work in a commercial setting is a must, with at least 3 years of experience; this is not a graduate role. We are seeking candidates with experience pulling, cleaning, and analysing data from production systems along with reporting and data visualisation. You should also be comfortable presenting findings to non-technical stakeholders, including senior leadership or customers. Experience working in or alongside teams building data-intensive products, ideally including ML or AI systems, is highly desireable.

You might be trained as a data scientist with a preference for data work and strong applied data and statistical skills, or come from an analyst background but with sufficient fluency in writing Python to build and own reporting and analyses independently. Healthcare experience is a plus but not required.

Technical skills

• Python for data work: pandas, NumPy, Jupyter, plotting libraries (matplotlib, Plotly, seaborn), and enough general Python to write small tools and scripts without help.
• SQL across common dialects, including reading and reasoning about non-trivial queries and joins.
• A modern BI or dashboarding stack (Metabase, Looker, Superset, or equivalent), sufficient to build and maintain dashboards without engineering help for most work.
• Basic statistical thinking: sampling, confidence, effect sizes, and distinguishing a meaningful difference from noise.
• Reading and interpreting evaluation outputs from AI systems: precision and recall, error taxonomies, and what model metrics mean for a non-specialist audience.

Personal attributes

• Communication-led: treats clear presentation as part of the analysis, not an afterthought.
• Pragmatic and outcome-focused, willing to own the analytical question end-to-end.
• Comfortable flagging data-quality issues early and shaping the question rather than only answering it.
• Cross-functional by instinct: works effectively across engineering, AI, commercial, and clinical colleagues.

Our hiring process

1. Introductory screening interview (30 minutes)
2. Interviews with senior leadership and cross-functional partners
3. Final interview and offer

Benefits

• Competitive salary
• Company pension
• 25 days of paid annual leave (pro-rata)
• Flexible hybrid working environment
• Employee Assistance Programme
• Modern, dog-friendly office near Chancery Lane with free drinks

Dyad's mission is to improve the delivery and efficiency of healthcare.

We are building a platform to model and manage the flow of information within healthcare organisations, improving outcomes for patients, payers, and healthcare providers. We believe data handling in current healthcare systems is needlessly complex and disconnected, leading to isolated and inefficient decision making. To showcase how this technology can advance the delivery of healthcare and improve lives, we build and deploy products for healthcare providers and payers into the UK and US markets.

Dyad is an energetic, health-tech startup, currently around forty employees. Our team is growing as we explore new markets and opportunities. We are passionate about technology and its applications in worthwhile ventures. New joiners will have a significant impact on the direction of the company, as well as our culture.

Our products

Dyad's Platform: Dyad's products are founded upon our Semantic AI platform, which enables payers and providers to access cutting-edge AI capabilities for their own use cases and applications. Our partners either use the platform APIs directly or work with us to develop applications for their use cases. For more information, please see our Platform page.

Primary care operations: Dyad develops a suite of products for healthcare operations, including:

• BetterLetter, our AI tool helping practices decrease their admin burden in processing clinical letters. We use this to reduce staff time spent identifying codes to be applied to the record as well as suggesting follow-up tasks and workflow optimisations. BetterLetter helps providers save time, save cost, improve performance under audit and build staffing resilience.