Forward-Deployed AI Engineering
YakData moves organizations from AI experiments to production operating systems. Definition through deployment is led directly by Stephen McDaniel, Netflix’s first data scientist and a repeated builder of first systems inside consequential organizations. In weeks, not quarters.
Entry engagement from $15,000, delivered in 7 to 10 business days. Direct founder response within 1 business day. No junior sales handoff.
01 · The approach
Most AI pilots die between the demo and the P&L. The model is rarely the problem: three harder jobs go undone, and no one person inside the building holds them. A forward-deployed AI engineer is that person, a senior architect-builder who works inside your environment, beside your executives and domain experts, and leaves behind a working system your team runs.
The pilot answers a question no executive owns. Without a sponsor, a measurable outcome, and acceptance criteria, a working demo is still an experiment.
Real data is fragmented, access is governed, and the workflow needs rules, human review, and monitoring. That is systems engineering, not prompting.
A system with no trained internal owner, no runbook, and no update cadence decays into shelfware. Handoff is a deliverable, not a hope.
One accountable principal across the full chain
Executive sponsor, decision framing, access, and the economics that justify the build.
Ambiguity becomes system requirements, acceptance criteria, and a fixed production plan.
A vertical system on your data: models, rules, human review, and the integrations that matter.
Production under client-controlled security, least-privilege access, and live monitoring.
Trained internal owner, documentation, runbook, and a clean closeout. You own the system.
Business value and operating decisions come before technology selection. Every time.
02 · Track record
Current systems first. The most recent deployment converts national television advertising data into forecasting, competitive intelligence, and media-allocation decisions across the Canadian market.
The challenge was not collecting more television data. It was turning thousands of shows, channels, and advertising events into a reliable operating system for forecasting, buy validation, and audience-value decisions.
Built the end-to-end data platform and predictive forecasting engine supporting Canada’s approximately $2.7B linear television advertising market. Independent economic impact modeling estimates the platform could enable approximately $183M in annual efficiencies for strategic agency partners.
Designed real-time campaign optimization workflows independently estimated to improve media performance by approximately $350K per $10M in advertising spend.
The $2.7B figure is the approximate annual size of Canada’s linear television advertising market. The $183M and $350K figures are modeled estimates from independent economic impact analysis, not audited client financials. Inputs, formulas, boundaries, and the line between what was modeled and what was directly observed are shared with qualified prospects.
Where should marketers and agencies place the next media dollar?
Approximately $2.7B in annual linear television advertising across Canada.
Forecasting, data engineering, audience modeling, exception handling, decision analytics.
30 national-scale forecasting and analytics projects across two years.
Automated analytical applications with structured multi-model validation under YakData’s 77 Rules review method, human checkpoints, and client-controlled data. Governed AI doing real work, not a chatbot demo.
Designed and built the stochastic revenue simulation engine the Investment Committee used to validate the valuation of a $1B+ pharmaceutical product-line acquisition.
Also built first systems for Navy Cyber · the Takeda Abbott
pharmaceutical alliance · a $10B+ European alternative asset manager · Series A to C
startups
Architecture breadth Snowflake · BigQuery · Redshift · Oracle · Teradata
· DuckDB · ClickHouse · frontier and open-source models
03 · Engagements
Priced in the open so a decision can be made in one meeting, not a procurement cycle. Serious problems only: minimum engagement $15,000 remote, $25,000 onsite.
Converts an ambiguous problem into a buildable system and a fixed-price production plan. The default first purchase.
$15,000 remote, fixed $25,000 onsite
Builds and deploys a working vertical system against written acceptance criteria, then transfers it to your team.
From $60,000 remote From $75,000 onsite
Sustained principal-level architecture and execution inside your team, for roadmaps bigger than one system.
From $30,000 per month, remote From $40,000 per month onsite
Every new client relationship begins with a paid System Definition, unless scope, access, acceptance criteria, and economic value are already unusually clear.
No free architecture disguised as sales. The first conversation establishes fit. Detailed system design, data evaluation, and implementation planning belong inside a paid engagement.
04 · Governance
YakData runs on a written operating constitution. These are the standards every engagement is delivered under. They are why the work survives security review, audit, and the departure of any single person, including me.
Scope, assumptions, and acceptance criteria are documented before the build. Changes go through decision gates, not silent drift.
Work happens inside your cloud, private environment, or local boundary. Data movement is minimized. Access can be time-bound and least-privilege.
I do not promise production outcomes gated by your access approvals, security review, or third-party procurement. I tell you where those risks sit on day one.
Where decision quality requires it, work is validated through structured multi-model review under the 77 Rules method. Applied where warranted, never sold as a guarantee.
Code, models, documentation, and deployment assets are structured for internal ownership. Open-source or client-controlled models where external providers create cost, privacy, or competitive risk.
Fixed prices tied to defined scope. A discount only ever comes with a matching reduction in scope, timing, access, or deliverables.
05 · Fit
Founder-led capacity is the constraint, so qualification is strict. Thirty seconds here saves both of us a meeting.
CIOs and CTOs with stalled pilots. CFOs and COOs under board pressure on planning. PE operating partners integrating acquisitions. VPs of data facing an executive deadline their team cannot absorb.
06 · The principal
No leverage model, no bait and switch. Every engagement is led and substantially delivered by Stephen McDaniel.
Stephen built and launched our new real-time US audience initiative, quickly deploying a robust Snowflake system. His skill in integrating key company data assets and delivering polished client presentations earned rave reviews from our executives, board, and strategic clients.
Stephen’s work has not only modernized our platform, but has been essential in bridging the gap between Cable TV and digital content. He combines strategic vision with hands-on expertise in data engineering, analytics, and data science. These traits make him an invaluable asset.
I highly recommend him.
Stephen had an immediate and substantial impact on our team after taking the reins as Head of Data Science. We were working on a client deliverable for a Fortune 500 company and Stephen was able to produce metrics and visualizations that we hadn’t envisioned before, that ultimately helped FanAI provide a much richer understanding of customer and fan behavior for the end client.
Stephen brings a calm and focused energy to meetings backed by a strong work ethic to achieve our goals. I would welcome the opportunity to work with Stephen again in the future.
Stephen is a master at understanding the business, identifying what key actionable insights are critical, and then building the analytical and technical systems to support that. Stephen looks at what the business needs and works back to the technology. This insight is invaluable for improving ROI and driving quick wins from the analytical investment.John LodmellCFO, Nordstrom Credit · LinkedIn recommendation
Stephen is a true Data and Analytics subject matter expert. He effortlessly taught, inspired, and took questions throughout the complete four-day course. I would welcome the chance to work with Stephen and highly recommend him as an expert in the analytics space.Diego SaenzGeneral Manager, Deloitte AI Advantage · Senior Managing Director, Deloitte · LinkedIn recommendation
References Recommendations on LinkedIn. Additional references are available to qualified prospects.
Pat Hanrahan built his 2012 Tableau Conference keynote around Stephen’s book The Accidental Analyst, saying it changed how he thought about analytics and reshaped how he taught it at Stanford.
PAT HANRAHAN, PhD
CANON USA Professor, Stanford University
Co-founder, Tableau Software
Founding employee, Pixar
Turing Award laureate · 3x Academy Award winner
Stephen McDaniel was Netflix’s first data scientist, building the subscriber lifetime-value and retention frameworks behind marketing, operations, and finance decisions. Across three decades he has built first analytical and decision systems inside consequential organizations: a $1B+ pharmaceutical acquisition model, Navy Cyber, an international bank’s governed AI applications, and national-scale television forecasting.
He wrote three of the field’s standard texts and taught forecasting and analytics as faculty and guest lecturer; credits below. YakData is that career pointed at one job: putting AI into production where it changes an operating result.
Director of Analytics, Tableau. Senior product and development leadership, SAS and Brio/Oracle.
Faculty: INFORMS, TDWI, AMA. Guest lectures: Princeton, Brown, Chicago Booth, U. Washington.
Writing Forward-Deployed AI, the working methodology behind these engagements. Field notes weekly on LinkedIn.
07 · Before the call
The operating model and boundaries are stated directly so the first conversation can focus on the business problem.
A consequential decision or workflow, an accountable executive sponsor, usable access to data and domain experts, meaningful urgency, and a realistic budget path.
Stephen personally leads discovery, business and domain translation, architecture, modeling, and the critical implementation path. The engagement is not sold by a principal and handed to a junior team.
Yes. Systems can be developed inside an agreed client-controlled cloud, private environment, or local deployment boundary, with external data movement minimized.
The agreement defines ownership explicitly. The usual objective is that you retain the code, architecture, documentation, model assets, and deployment knowledge required to operate independently.
Yes. Provide the decision, desired outcome, current systems, timing, and estimated budget. YakData will identify the likely starting engagement and whether a proposal discussion is justified.
YakData’s proprietary review protocol: structured multi-model validation with documented review rules and human checkpoints, applied where decision quality warrants it. A delivery standard, never a guarantee. The full method is shared inside engagements.
You receive a direct next step: no fit, more information needed, a paid Rapid System Definition, or, for unusually clear and bounded work, a deployment proposal path. Proposals arrive within 2 business days.
08 · Start
Describe the decision or workflow, what should be true instead, and why it matters now. Choose a 20-minute fit call or an initial scoped quote.
Stephen reviews every qualified inquiry personally. If it is not a fit, you get a direct no and a pointer somewhere better.
A one-page System Definition proposal: scope, deliverables, acceptance criteria, price, and start date. Sign it or decline it.
You leave with a system spec, economics, and a fixed-price production plan, whether or not you build with me.
Founder-led means the person who scopes the work remains accountable through delivery and handoff.
LinkedIn: mcdanielstephen
