You need a right hand who's technically strong and commercially sharp - someone who can go deep with data scientists and engineers, and still be the trusted voice in a C-suite room. That's the seat I'm built for.
Each section answers a specific line item in the JD. Jump to whatever you need.
You need someone who can identify and prioritize AI use cases, direct technical teams end-to-end, establish governance and operating models, and act as a trusted advisor to senior client executives - all while contributing to the commercial agenda.
That's not a job description for me to grow into. It's a description of what I already do. For the last five years I've been the solutions consultant for our enterprise customers - translating business problems into technical implementations and then owning them from pipeline to production.
Today I directly manage relationships with C-suite leaders on partner accounts worth $1.2M+, and I own the product roadmap while managing the data analytics and engineering team that delivers it.
What I'd bring to PwC is the exact pairing the practice asks for: technical depth that earns the room's respect with the engineers, and commercial instinct that earns it with the CFO.
Designed and shipped Model Context Protocol servers that connect LLMs to internal systems - the connective tissue most enterprises are still figuring out.
Built conversational AI grounded in proprietary data - including this one. Not demos: production tools that change how teams work and how customers buy.
Stood up the operating backbone - intake, prioritization, delivery cadences, measurement. The boring layer where AI value is actually realized.
Pipelines, modeling, dashboards, decisions. I sit close enough to the data team to debug a query, and close enough to the customer to know what the answer is worth.
I own what we build, why, and in what order - discovery → ship → measure → iterate, with quantified business impact attached to each bet.
From use-case identification through model deployment and value realization - the same arc the JD describes, run multiple times.
5+ years of direct relationships with C-suite buyers on partner accounts worth $1.2M+. I'm the person they call before they call procurement.
Every engagement I run starts with the business outcome - revenue, cost, risk, time - and works backward into the model, the data, and the change plan. The JD calls this 'translate AI ambitions into measurable business outcomes.' I call it Tuesday.
Reported sales progress and GTM motion weekly to private investors with $50B+ in transactions behind them. If our forecast was off, I heard about it. That bar is now baseline for me.
I've owned the full GTM motion - qualifying opportunities, shaping solutions, leading proposals, and then delivering. The same muscle PwC's commercial agenda asks for: identify, qualify, propose, expand.
When I took over the engineering team, they hadn't shipped code in six months. Not from lack of effort — they were constantly busy — but the work wasn't moving. Pull requests were stacking up as a stand-in for progress, commitments were slipping, and everyone was busy in a way that produced nothing a customer could see.
I don't write code — I architect the process and decisions around it. I reset the scrum cadence, rewrote the roadmap so priorities were clear and defensible, and attacked the operating model that was blocking the talent. Within that reset, the team started shipping. The product I relaunched scaled past 11,000 accounts in eighteen months and lifted revenue 20%.
The way I stay close is through functional specification. I sit between the client need and the engineers, writing the spec that translates between them — what we're building, why, and in what order — so the team moves on a clear increment instead of guessing at intent. Then I own delivery end to end: discovery, spec, build, ship, measure, iterate. I'm not in the codebase. I'm in the seams — where business intent gets lost on the way to engineering, and where customer value evaporates. That's the layer I hold together.
The clearest example is our Model Context Protocol server, shipping this year. I'm not building it — I'm leading the thinking on how it gets implemented responsibly. I advise the executive team on data protection and identity management, make the calls on access boundaries and risk before architecture decisions happen, and translate client needs into the specs the engineers build against. Governance stays in the room from day one, not bolted on at the end. When you read "directs technical teams end-to-end" in your job description — that's not something I'd grow into. That's the last five years of my work.
The JD calls for someone who can establish responsible AI, model risk management, and data governance. That sits at the intersection of where I trained and where I work. My M.S. in Information Technology with a major in Cybersecurity from Lawrence Technological University was built for exactly this moment - the years when enterprises move from AI experiments to AI in production, and the governance question stops being theoretical.
I think about data lineage, access boundaries, and identity exposure before I think about model selection. It's the only order that scales.
Frameworks for evaluation, drift monitoring, human-in-the-loop, and audit trails - built so they survive a regulator, a board, and a bad day.
Intake, prioritization, delivery cadence, value measurement. The boring scaffolding that decides whether AI investments compound or evaporate.
I'll talk to you like we already work together - easiest way to show you how I think.
You're growing the AI & Advanced Analytics practice in Western Canada. That means two clocks running at once: delivery quality across concurrent engagements, and pipeline that compounds. My job as your right hand is to make sure neither one slips while we scale.
This is a starting hypothesis, not a finished plan. Show me the good, the bad, and the ugly of where the practice is today, and the plan gets sharper.
"First I need to see the real picture - engagements, pipeline, team, clients."
"Earn the room before reshaping it."
"Build the system that lets the practice scale past me."
This chatbot is trained on my background - technical depth, commercial track record, leadership style, and how I'd think about Western Canada AI. Ask it anything you'd ask me in an interview.
Hi Danielle — ask me anything about Sierra's experience, leadership style, or why she's drawn to this role at PwC.
One call. If by the end you don't see the fit, we both walk away with our time well spent. If you do - we get to work on Western Canada.
Book a 30-min call →