Theresa O'Kon

Why this persona fits the audience

Owns product ops for an 18-person fintech app, assembling release notes, weekly KPI packs, and experiment summaries from Amplitude, Jira, and Excel. She coordinates three PMs and two analysts, and the team already pays for a stack of collaboration and reporting tools.

• Age: 39
• Location: Manchester, UK
• Occupation: Product operations manager
• Income: £88,000
• Education: MSc in Data Science
• Company size: 18 people (Series A stage)
• Years in role: 4 years

“If you can't measure it, document it, or automate it, it doesn't exist in this company.”

Theresa O'Kon is the operational backbone of a burgeoning fintech startup in Manchester, a role she has held for four years since joining the company at just 18 employees. With an MSc in Data Science from the University of Manchester, she transitioned from pure research roles into operations, driven by a fascination with how data structures influence human behavior and business outcomes. At 39, she commands significant respect within her organization, not through loud assertions of authority, but through the undeniable reliability of her systems. She built the company's current reporting infrastructure from scratch, transforming a chaotic collection of spreadsheets into a semi-automated pipeline that feeds the CEO's investor decks. Despite her technical prowess, Theresa operates in a state of perpetual resource constraint. The company has a budget for standard tools like Amplitude, Jira, and Slack, but lacks the funds for enterprise-grade orchestration platforms. This forces her to act as a human API, constantly stitching together data sources using Python scripts and manual Excel manipulation. She recently made the difficult decision to reject a proposal from a flashy new AI startup, citing a lack of transparency in their data handling and an over-reliance on 'black box' algorithms. Instead, she championed a modest upgrade to their existing SQL infrastructure, a move that saved £15,000 annually while improving query speeds. Her personal life reflects her professional philosophy: structured, efficient, and devoid of unnecessary noise. She lives in a meticulously maintained home in Didsbury, where every item has a designated place. She views time as a finite currency, fiercely guarding her evenings and weekends from work intrusions. While her income of £88,000 places her comfortably in the upper-middle class, she remains frugal, investing heavily in her retirement fund and avoiding lifestyle inflation. To Theresa, success is not about rapid growth at any cost, but about building a sustainable, predictable engine that allows her team to do their best work without drowning in administrative overhead.

Background & daily life

A day in their life

Theresa wakes at 6:15 AM, brewing a strong pot of coffee while checking the overnight server logs on her phone to ensure no critical alerts were missed. By 7:45 AM, she is cycling to her office in central Manchester, listening to a podcast about behavioral economics. At 9:00 AM, she sits down to consolidate the nightly data dumps from Amplitude and Jira, a ritual that takes until 11:00 AM. Her mid-morning is reserved for a sync with the three Product Managers, where she mediates a dispute over feature prioritization using a shared spreadsheet model. Lunch is eaten at her desk while reviewing the draft release notes. The afternoon is fragmented by ad-hoc requests from the CEO and two hours of deep work updating the executive dashboard. She leaves the office promptly at 5:30 PM, refusing to check email after 6:00 PM unless it's a true emergency, spending her evening cooking dinner and winding down with a book.

Lifestyle

Theresa lives in a sunlit, renovated Victorian terrace in Didsbury, Manchester, where she values order and quiet. Her weekends are a mix of practical maintenance—garden pruning, meal prepping for the week, and cat care—and solitary intellectual pursuits like reading non-fiction or visiting art galleries. She is financially prudent, having paid off her student loans early, and prioritizes savings over luxury travel, preferring high-quality home goods instead. Socially, she maintains a tight circle of friends from university and work, favoring deep one-on-one conversations or small group dinners over loud parties. Health is maintained through regular yoga and cycling, which she uses to decompress from the mental load of managing complex data streams.

Work life

Her days are a relentless cycle of synthesizing chaos into clarity. As the glue holding an 18-person fintech together, she spends mornings consolidating data from Amplitude, Jira, and spreadsheets to build the weekly KPI pack that the CEO presents to investors. Afternoons are spent coordinating three Product Managers and two analysts, translating their conflicting priorities into a coherent roadmap. She is the gatekeeper of information, ensuring release notes are accurate and experiments are documented. The pressure comes not from deadlines, but from the sheer volume of unstructured data; if she stops, the company's operational rhythm slows, creating a bottleneck that feels like a physical weight on her shoulders.

Relationships

She is deeply embedded in her local community, often volunteering to judge local science fairs. Her relationship with her partner is built on mutual respect for each other's professional ambitions, though they occasionally clash over who manages the household finances. She looks up to her former thesis supervisor for career advice but finds most peer interactions limited to industry-specific LinkedIn groups. She is the go-to person for conflict resolution among her team, valued for her ability to remain neutral and factual when tensions rise between engineers and designers.

How they decide

Theresa employs a highly analytical, risk-averse decision style. She rarely acts on intuition alone; instead, she gathers data points, creates small-scale prototypes or pilots, and seeks consensus from key stakeholders before committing resources. She is skeptical of broad claims and requires concrete evidence of efficacy. While she can be decisive once the data aligns, she moves slowly, preferring a verified path forward over a fast, uncertain one. Peer validation is important, but only if backed by logical consistency.

How they sound

Theresa speaks with a crisp, measured Mancunian accent, avoiding slang and hyperbole. Her cadence is steady and deliberate, pausing to ensure precision in her vocabulary. She favors passive voice when describing processes to depersonalize blame but switches to active voice when assigning accountability. She avoids words like 'game-changing,' 'revolutionary,' or 'magic,' finding them indicative of low substance. Instead, she uses terms like 'robust,' 'scalable,' 'verified,' and 'frictionless.' She does not interrupt, but her silence during a conversation often signals that she is analyzing the logic of the speaker rather than simply listening.