Insight and Analytics Lead (Pharma)

KAI Conversations is an AI-powered conversation analytics platform used by some of the world's largest pharmaceutical companies to improve the quality and effectiveness of their customer interactions. The KAI platform supports multiple languages, integrates with enterprise systems, and enables large organisations to understand and improve the impact of their customer conversations.

We turn conversation analytics and CRM data into insight that pharma brand teams, local operating companies, field sales managers, and Reps/MSLs can act on. We're looking for an analyst who can take on almost any BI or insight challenge a client raises — a new dataset, a new audience, a new question - and work it through end to end, from raw data to a report that changes what someone does next.

The hardest calls here aren't statistical, they're judgment calls: knowing when a finding is clinically or commercially plausible rather than just tidy-looking, understanding how field force, marketing, and market access functions actually operate, and knowing what “good” looks like in a pharma commercial context. That judgment is what separates a correct-looking chart from a finding a client can act on.

You'll use modern AI tools (Claude, Copilot, ChatGPT) as core working infrastructure for analysis and reporting - and help make that work reliably and repeatably, so good analysis isn't rebuilt from scratch each time. That's one capability among several: you'll flex across whatever data sources, stakeholders, and formats a client challenge calls for.

As this work matures into repeatable, automated outputs, you'll also help shape dashboard concepts for different stakeholders - brand marketing, LOCs, field sales, Reps/MSLs - working with our UI/UX team to bring them to life. No design tool experience (e.g. Figma) is needed; what matters is knowing what each stakeholder needs to see, and communicating that clearly to the people who build it.

What kinds of questions you will be answering?

The specifics vary by client and brand, but here are some examples:

• Which objections are actually blocking adoption of a therapy, and how well are reps resolving them in the room?
• What do patients themselves raise as concerns or barriers, and where does that create friction in the care pathway?
• What separates a brand's best-performing reps from the rest, in terms of what they actually do differently?
• How would linking CRM and prescribing data change what we could confidently tell a brand team next quarter?

You should expect to move between questions like these regularly, often for different brands and different stakeholder audiences in the same week.

Key Responsibilities

Analysis, across a genuinely broad remit

• Combining conversation analytics with CRM data (Veeva, Salesforce, and others) to answer questions neither source can answer alone
• Auditing new datasets end to end, flagging data quality issues before drawing conclusions
• Adapting to whatever BI challenge a client brings — new data, new questions, new formats — rather than a fixed playbook
• Using AI tools (Claude, Copilot, ChatGPT) to explore datasets efficiently, while remaining the check on whether a finding actually holds up

Judgment and rigor

• Sense-checking findings against real commercial and clinical context — treatment pathways, competitor dynamics, field operations, compliance — not just the data itself
• Reconciling disagreements between metrics or data sources by understanding what each actually captures
• Saying when a finding doesn't hold up, and why, rather than presenting something tidy but hollow

Reporting, storytelling, and stakeholder work

• Producing strong, infographic-style visuals designed to make a specific finding land clearly, not default charts
• Reframing the same findings into different reports for different audiences — brand marketing, LOCs, field sales, Reps/MSLs — without distorting the evidence
• Presenting findings to stakeholders as part of a team, defending methodology under challenge

Building reliable, repeatable capability

• Turning proven analysis approaches into structured, documented, reusable methods, reducing rebuild effort for each new client or dataset
• Owning prompt design and refinement for AI-driven analysis, treating reliability as engineered, not assumed
• Documenting known pitfalls and methodology decisions as they're discovered

Product & dashboard development

• Conceiving dashboard concepts for different stakeholders — brand marketing, LOCs, field sales, Reps/MSLs — defining what each needs to see to make a decision
• Working with KAI's UI/UX team to translate concepts into usable designs — owning content and analytical logic, while UI/UX owns visual craft
• The natural end point of the repeatable-methods work: moving from a bespoke, rebuilt-each-time report to a standing, automated dashboard
• Iterating dashboard concepts based on real stakeholder feedback and usage

Required Experience & Skills

Pharma commercial/marketing foundation - essential

• Direct pharma commercial, marketing, or brand experience, or an analytics career embedded in pharma commercial teams
• Understanding of pharma commercial functions - brand planning, field force models, HCP engagement, market access
• Enough clinical/commercial fluency to judge whether a finding is plausible before it's presented as fact

Analytical

• Degree level in a numerate discipline with strong statistical grounding (statistics, mathematics, data science, economics, or similar), or equivalent experience
• Strong hands-on Python and/or SQL experience for data manipulation and analysis
• Experience turning messy data into defensible findings, flagging thin samples and not overstating results
• Comfortable with structured qualitative classification of free-text data where automated methods fall short, and transparent about it

AI-directed analysis

• Hands-on experience using LLM tools (Claude, Copilot, ChatGPT) for substantive analysis, not just drafting or summarising
• Understanding of how to structure reliable, repeatable AI instructions, with interest in developing this further

Product thinking

• Comfortable conceiving dashboard concepts for different stakeholders, defining what an audience needs to see and why
• Able to communicate a dashboard concept clearly enough for UI/UX to build from

Reporting & visualisation

• Excellent Excel skills, including VBA
• Strong Power BI skills, including Power Query (M) and DAX
• Strong ability to design infographic-style visuals that make a finding clear at a glance
• Judgment on which format suits which stakeholder and question

Adaptable

• Able to reframe the same evidence for different audiences without changing the facts
• Confident presenting analysis to stakeholders and clients, including under challenge
• Strong written communication for polished, client-ready documents
• Structured and methodical - audit before build, confirm before present
• Takes challenge on methodology well, pushing back when a request would compromise analytical integrity
• Comfortable with variety - energised by new problems rather than a fixed, repeatable brief

Desirable Experience

• Familiarity with Veeva, Salesforce, or other pharma CRM platforms
• Experience with prescribing/Rx data sources (e.g. IQVIA, Symphony)
• Background in a specific therapeutic area relevant to KAI's client portfolio

Working Environment

• Candidates must be UK-based and eligible to work in the UK
• Primarily remote within a distributed team. Access to co-working space if preferred
• Able to attend team meetings in London two days per month when necessary
• Occasional travel may be required for client meetings or programme activities

What Success Looks Like

Within the first 3 months, the successful candidate will:

• Have independently delivered end-to-end analysis and reporting for multiple client business questions, from raw conversation/CRM data through to a stakeholder-ready report
• Have built working fluency with KAI's conversation analytics data and at least one CRM platform (Veeva or Salesforce), including how to join and cross-validate findings across them
• Have produced reports tailored to at least two different stakeholder audiences (e.g. brand marketing and field sales) from the same underlying analysis, adapting tone and focus appropriately for each
• Have contributed to turning at least one piece of proven analysis into a documented, repeatable method
• Have contributed to a future dashboard concept, developed alongside the UI/UX team
• Have presented analysis directly to a client or senior internal stakeholder as part of a delivery team

Career Progression

This role sits at a stage comparable to a mid-level Business Insights & Analytics Lead career point - a strong foundation for progressing toward broader analytics leadership of a small team, combining deepening technical capability, growing scope across clients and stakeholders, and increasing involvement in shaping KAI's own product.