Sheri Armstrong

Why this persona fits the audience

Operating as a sole trader business analyst in London, manually scraping company career pages and cross-referencing LinkedIn with public filings to identify genuine contract opportunities that avoid generic job board spam.

• Age: 34
• Location: London, UK
• Occupation: Freelance Business Analyst
• Income: £65,000 - £85,000 (variable)
• Education: Bachelor's Degree in Economics
• Company size: Sole Trader (1 employee: herself)
• Years in role: 7

“The best opportunity isn't the one advertised on the front page; it's the one hiding in the footnotes of a quarterly report that no one else bothered to read.”

Sheri Armstrong, 34, is a London-based freelance Business Analyst operating as a sole trader. A graduate of the London School of Economics with a degree in Economics, she transitioned from full-time corporate roles seven years ago after becoming disillusioned with the inefficiencies of traditional recruitment channels. She now commands a variable income between £65,000 and £85,000, derived from short-to-medium-term contracts primarily within the fintech and retail sectors. Her business model is built on a unique value proposition: she identifies high-quality contract roles that are never posted on mainstream job boards by manually scraping company career pages and cross-referencing them against public filings and LinkedIn data. This 'detective work' allows her to bypass the noise of generic job board spam and secure direct introductions with hiring managers. Despite her technical prowess in Excel automation and basic Python scripting, she remains wary of paying for expensive SaaS solutions without tangible evidence of their utility. Her recent decision to build custom scrapers rather than subscribe to existing aggregators reflects her desire for total control over her data integrity and lead generation process. She is driven by a need for autonomy and a fear of obsolescence, constantly refining her skills to stay ahead of AI-driven changes in the analyst landscape. Her professional reputation is her most valuable asset, meticulously maintained through punctuality, rigorous data accuracy, and a network of trusted peers who vouch for her ability to deliver insights quickly.

Background & daily life

A day in their life

Sheri wakes at 5:45 AM, avoiding the rush hour commute by working the first two hours of her day when her mind is sharpest. She starts with a quick run around Regent's Park, followed by a cold brew coffee and a review of her overnight email queue. By 8:00 AM, she is deep in a client project, likely debugging a VBA macro or analyzing a dataset for a retail chain. Lunch is a quick sandwich eaten at her desk while scrolling through a curated list of FTSE 100 career pages. Afternoon hours are reserved for video calls and strategic planning. Late afternoon is dedicated to her 'hunting' ritual: scraping new job postings, cross-referencing them with Companies House filings, and updating her CRM. Evenings are usually reserved for learning new automation techniques or winding down with a book, ensuring she remains competitive in a fast-moving market.

Lifestyle

Sheri lives in a converted warehouse loft in Shoreditch, sharing the space with two other creatives who tolerate her early morning spreadsheets. Her routine is rigid; she treats her home office like a high-stakes trading floor, complete with three monitors and a standing desk. She is an avid runner, using the city's parks to clear her head before client calls. Financially, she lives slightly below her means, aggressively saving into a private pension while keeping a liquid emergency fund equivalent to six months of expenses. Socially, she curates her circle tightly, preferring deep conversations at independent coffee shops over loud networking mixers, though she attends quarterly industry meetups to maintain her reputation.

Work life

As a sole trader, Sheri spends roughly 30% of her week hunting for work and 70% delivering analysis. Her primary tools are Excel, Python scripts for web scraping, and a complex Notion dashboard tracking her pipeline. She avoids recruitment agencies entirely, viewing them as gatekeepers who dilute her rates. The pressure is constant; there is no HR department to shield her from late-night emails or the anxiety of the 'gap' between contracts. Her days are fragmented by client meetings, invoice chasing, and the relentless maintenance of her own lead generation systems. She prides herself on being the person who finds the jobs others miss, often spotting roles buried in obscure corporate filing cabinets before they hit major boards.

Relationships

Sheri maintains a close bond with her sister in Manchester, whom she calls every Sunday. Her professional network is her primary social currency; she relies heavily on a core group of five former colleagues who act as her referral engine. She is married to a software engineer, a partnership built on mutual respect for autonomy and shared intellectual curiosity. While she has a few drinking buddies, she is careful not to let personal relationships bleed too much into her business inquiries, maintaining a professional boundary that sometimes makes her appear aloof to new acquaintances.

How they decide

Sheri operates on a principle of 'verify then invest.' She rarely makes impulsive decisions regarding money or time. Before committing to a new strategy or tool, she conducts a cost-benefit analysis, often running a pilot phase or building a rough prototype herself. She values peer validation and case studies over marketing copy. If a solution requires significant upfront cost, she demands proof of ROI based on data she can replicate. She is skeptical of 'black box' solutions and prefers transparent, modular tools that allow her to audit the underlying logic.

How they sound

Her speech is precise, articulate, and devoid of filler words. She uses British English with a slight Cockney inflection that softens her otherwise technical delivery. She speaks in active voice, focusing on outcomes and metrics. She avoids jargon unless necessary, preferring plain English to explain complex data concepts. In conversation, she listens intently, often pausing to formulate a logical response rather than rushing to fill silence. She rarely uses hyperbole, instead grounding her statements in observable facts.