I find the real problem, then build the AI that fixes it.
I'm Brendan Mair, a product-minded analyst in Austin, Texas. I've eliminated $3M+ in vendor costs, built an agentic process that cut downstream reporting failures by 90%, and become the go-to person for engineering, compliance, product and client teams.
Research featured inFindings on busing programs from my data work at IMUMI
- The Wall Street Journal
- The New York Times
- Reuters
What I bring
Four strengths, proven in the numbers.
The four things I'd do on your team, each with the result that proves it.
i.AI & automation
AI and automation
I build automation and AI agents that catch problems and fix them before anyone has to step in.
90%
fewer downstream reporting failures Apex Fintech Solutions
- AutomatedManual payment operations workflows
- 20%faster invoice processing through automated billing
ii.Product
Product thinking
I turn regulations, constraints and messy requirements into specs engineers can build, then see them through rollout.
$3M+
in vendor costs eliminated Apex Fintech Solutions
- BuiltKPI and SLA frameworks for new payment features
- 200%more participation after launching 3 new programs
iii.Problem solving
Problem solving with data
I trace failures to their root cause in the data and fix them for good, not just the report that showed them.
200M+
records across 10M+ accounts Apex Fintech Solutions
- $240B+a year through the platform
- 12+immigration datasets analysed
iv.Collaboration
Collaboration and leadership
Engineering, compliance, product and client teams bring me in because I can speak each of their languages.
Twice
Quarterly Recognition Award, in a single year Apex Fintech Solutions
- End to endPayment operations initiatives, scoping to rollout
- Go-toOperational resource for engineering, compliance, product and client teams
Selected work
Projects where I own the whole problem.
Each case shows the problem, my role, what I did and the outcome, next to the artifact I built.
All projects01 Relay · Payment operations
Explain every failed payment, and route around a failing processor.
- Problem
- When a payment fails, operations teams can't see why, and a failing processor keeps taking traffic until someone notices.
- My role
- Product owner and builder: scope, metrics, decision records, build and measurement.
- What I did
- Built a two-processor payment platform with one timeline per payment, plain-language failure explanations, automatic failover, incidents, dashboards, signed webhooks and a Python SDK.
- Outcome
- Complete on synthetic data: in a scripted processor outage, failover recovered most of the payments a static setup lost (simulated; figures on the project page).
Fig. 1 — Mock-up of the Relay console. All payments and processors are synthetic. 02 Regional Sales Data · Excel, SQL, Tableau
Which sales channels actually make money, region by region.
- Problem
- Revenue showed which channels sold the most, not which ones earned the most once costs were counted.
- My role
- Analyst, end to end: question, data, dashboard, recommendation.
- What I did
- Cleaned and joined the sales data in Excel and SQL, then built profitability views by channel and region in Tableau.
- Outcome
- [2–3 findings from the deck] — owner to confirm
Top channel[channel]Margin gap[n] ptsProfit margin by channel All regions
Highest marginAverageFig. 2 — Illustrative shape only. Channel names and values come from the Tableau workbook: owner to confirm. 03 Congress and Voter Behavior · R
Does social pressure get people to vote, and by how much?
- Problem
- Turnout campaigns use social pressure, but how much it changes behaviour is rarely measured against a control group.
- My role
- Analyst: research question, statistical analysis in R, presentation.
- What I did
- Compared turnout across treatment groups and a control, estimating each effect with a confidence interval.
- Outcome
- Largest effect: [group], +[n] pts turnout — owner to confirm
lm(turnout ~ treatment) # effect vs control
Group 1+[n]Group 2+[n]Group 3+[n]Group 4+[n]Fig. 3 — Illustrative shape only. Groups and estimates come from the analysis: owner to confirm.
Experience
Same instinct in every role: fix it at the source.
Career so far
2018 – today- Payment Product Operations SpecialistWise · Sep 2026 – now
- Business AnalystApex Fintech Solutions · Mar 2025 – Sep 2026
- Immigration Data Analyst InternIMUMI · Jun – Nov 2024
- Program CoordinatorUC San Diego Recreation · Oct 2021 – Jul 2024
- Legal InternMichigan Law Center · Sep 2018 – Aug 2021
About
I started on the operations side: running programs and their budgets, then analysing migration data that ended up in national news.
Today I work in payment product operations. The thread is the same: find why a problem keeps coming back, fix it at the source, and leave a tool the team can run without me.
Now
November 2026- Building RelayA payment operations platform, on synthetic data
- Building AI toolsAgents and skills with Claude Code
How I work with people
Teams bring me in to translate.
Most of my work sits between engineering, product, compliance and operations. These are the habits that make it land.
Translate between teams
I turn system behaviour into business language, and business needs into specs engineers can build.
Align stakeholders
I bring the people affected to one written decision before the work starts, with the trade-offs on the page.
Hand off cleanly
I document, train and hand finished work to the team that runs it, so it doesn't depend on me.
Mentor
I pair with teammates on SQL, tooling and triage, and share what I learn as I go.
Tools I use
- SQL
- Python
- R
- Tableau
- Power BI
- Excel
- Claude Code
- Power Automate
- Jira
- Confluence
- GitHub
Let's talk