Bo Peng

Short bio

Bo Peng founded Precision Delivery Federal in 2026 after fifteen years building data and software systems for federal agencies through three separate federal consulting firms. His focus is the hard part of delivering AI inside the U.S. government: production-grade machine learning and cloud infrastructure that actually meets ATO requirements, passes security reviews, and produces measurable mission outcomes.

Bo is a Kaggle Top 200 competitor, placing in roughly the top 0.1% of more than 200,000 active data scientists worldwide. He has held three federal consulting firm tours, shipped production ML at SAMHSA, migrated federal health data platforms to AWS GovCloud, and taught more than 400 university students across fifteen classes in 2025. Every Precision Federal engagement is founder-led — Bo personally writes the code, owns the architecture decision, and sits on the delivery calls.

Career arc

Bo's career has moved steadily toward higher-leverage federal work: from straight software engineering, into data engineering, into applied machine learning, and finally into full-stack AI systems that combine modeling, infrastructure, and interface. The thread through all of it is that production always comes first. A model that cannot be deployed into a FedRAMP-authorized environment is an interesting research artifact — not a federal deliverable.

Three federal consulting firm tours

Bo has served as a senior technical contributor at three separate federal consulting firms over the last eight years, supporting agencies across health, defense, and intelligence mission areas. These tours gave him direct exposure to how federal delivery actually works: the procurement cycles, the ATO process, the interplay between program managers and technical leads, the constraints of working inside GovCloud, and the difference between a pilot that impresses at a demo and a system that survives its fifth quarterly audit.

Production ML at SAMHSA

At the Substance Abuse and Mental Health Services Administration, Bo led development of production machine learning pipelines operating on federal health survey data. The work covered the full lifecycle: data ingestion from S3, transformation in Databricks, feature engineering, model training, validation against bias and fairness criteria, and deployment behind governed endpoints that federal analysts and contracted researchers could query. The pipelines continue to run today and form part of the agency's standard analytic capability.

Federal health data platform

Bo led cloud migration work that moved a federal health data platform from legacy on-premises infrastructure into AWS GovCloud, with a boundary definition that allowed the agency to pursue a FedRAMP Moderate authorization path. The migration preserved a decade of historical research data, modernized the ingestion layer, introduced infrastructure-as-code for every component, and eliminated roughly sixty percent of the operational toil that had accumulated in the legacy system.

Kaggle Top 200 — and why it matters for federal

Kaggle is the global competitive data science platform, with more than 200,000 active competitors worldwide at any given time. Bo ranks in the Top 200 overall — roughly the top 0.1% — as kaggle.com/bopengiowa. That ranking is not a certification. It is public, auditable evidence that Bo repeatedly beats out thousands of professional data scientists on problems that require feature engineering, model selection, ensembling, cross-validation discipline, and ruthless attention to leakage and bias.

Why that matters for federal AI work: the government is full of evaluations where the modeling is the easy part and the hard part is defending your method against a skeptical reviewer. Kaggle production-grade finishes are built on exactly that skill — producing a result that holds up when someone else tries to find the hole in your pipeline. The same discipline carries directly into SBIR Phase II technical reviews, NIST 800-53 control evidence, and model risk management documentation.

Teaching and public work

Bo spent five years as an adjunct professor at Indiana Wesleyan University, where in 2025 alone he taught 400 students across fifteen classes covering data science, machine learning, Python engineering, and analytics. The teaching load was voluntary on top of full-time federal consulting work. The discipline of having to explain a gradient boosting model to a classroom of working adults — and then grade the code they wrote on the spot — is a forcing function for clarity that every federal technical lead should experience at least once.

Bo publishes regularly on Kaggle, shares open notebooks demonstrating technique on public datasets, and writes plainly about the gap between academic ML and production ML. Federal agencies can see, before signing any agreement, exactly how he thinks.

Technical depth

Precision Federal builds systems end-to-end. That requires a technical leader who is current on every layer of the modern stack, not a consultant whose last production commit was five years ago. Bo's working stack includes:

• Languages: Python (primary), TypeScript, SQL, a working command of Rust and Go for performance-sensitive components.
• Machine learning: PyTorch, scikit-learn, XGBoost, LightGBM, Hugging Face Transformers, LangChain, custom agent frameworks. Experience with RAG pipelines, fine-tuning, and model evaluation harnesses.
• Web and API: FastAPI, Next.js, React, Tailwind, shadcn/ui. Full-stack from database to deployed frontend.
• Data: PostgreSQL, Databricks, Spark, DuckDB, Pandas, Polars. Data modeling for analytic and operational workloads.
• Cloud: AWS GovCloud (primary), Azure Government, Cloudflare. Terraform for infrastructure-as-code. ECS, Fargate, Lambda, S3, RDS, EKS.
• Security and compliance: NIST 800-53 control implementation, FedRAMP boundary definition, audit logging, SSP authoring, POA&M management.
• Tooling: GitHub Actions, Docker, Kubernetes, observability stacks built on OpenTelemetry.

How Bo works

Build end-to-end

Bo does not hand off. A Precision Federal engagement does not have a "solution architect" who draws a diagram, a "delivery lead" who schedules the work, and an offshore engineer who actually writes the code. Bo writes the code. He owns the architecture. He runs the production incident at two in the morning if it happens. That is what founder-led delivery means.

Ship before slides

Every Precision Federal engagement starts with working software as soon as humanly possible. That might be a three-page prototype on day two of a kickoff. It might be a functioning ingestion pipeline before the SOW ink is dry. The point is to move the conversation from hypothetical to demonstrated as fast as possible. Federal program managers have seen enough slide decks. They are starved for things that actually run.

Honest scope

Bo will tell a prospective customer that a capability cannot be delivered for the proposed budget, or that a proposed solution will not pass an ATO, before the contract is signed. This is unusual in federal consulting and it is deliberate. The industry norm of saying yes first and renegotiating later creates the failed projects everyone complains about. Precision Federal refuses to participate in that pattern.

Write it down

Every design decision is recorded. Every tradeoff is documented. Every control gets an evidence artifact. If Bo is hit by a bus, another engineer can pick up the repository and continue. This discipline is required for federal work. It is also just good practice.

Why founder-led delivery matters for federal

The traditional federal consulting model separates sales, architecture, and delivery across three different people with three different incentive structures. That separation is the source of most federal IT failure. The person who sold the scope is not the person who has to build to it. The person who architected it never has to debug it. The engineer who has to debug it never had a chance to push back on the architecture.

Precision Federal collapses that structure. Bo is the sales conversation, the architecture, and the delivery. There is no handoff tax. There is no senior-engineer rate being charged for a junior engineer's output. There is no "we need to bring in our principal" meeting because the principal is already the one writing the code.

This is not a model that scales to a thousand people. It is a model that produces excellent outcomes on a specific class of problem: federal AI systems that need deep technical judgment, fast iteration, and direct accountability. For that class of problem, founder-led is not a limitation — it is the feature.

FAQ

Does Bo hold a security clearance?

Not currently. Bo operates at the public trust and sensitive-but-unclassified level and has done so across all three prior federal consulting tours. For engagements that require SECRET or TOP SECRET access, Precision Federal partners with cleared primes who hold facility clearances and sponsor cleared personnel. The path is explicit: Precision Federal handles unclassified AI and data engineering work under direct contract; classified components are delivered through a cleared partner teaming arrangement. This is openly disclosed in every conversation where clearance is a factor.

Is Bo a U.S. citizen?

Bo is a lawful permanent resident on a documented path to citizenship. His residency status is fully compatible with SAM.gov registration, SBIR Phase I and II eligibility, unclassified federal contracting, and NAICS 541512 work. Some 8(a) and set-aside programs require citizenship and will become available in the future. Every engagement is scoped to match Bo's current eligibility.

Where is Bo based?

Ames, Iowa, 50010. Precision Federal is a fully remote-capable operation with clients across multiple time zones. Travel to Washington D.C., customer sites, and federal facilities is normal and expected.

Can I hire Bo directly, or does the work always go through Precision Federal?

All contracted work goes through Precision Delivery Federal LLC. Bo does not take side contracts, does not moonlight, and does not sub-contract personal work through other firms. This is a deliberate choice to keep liability, insurance, and compliance all under one roof.

What engagements is Bo not a good fit for?

Pure slideware strategy work where no system is being built. Very large-scale staff augmentation where a prime already has dozens of seats. Classified program delivery without a cleared prime partner. Projects where the customer needs a four-hundred-person integrator with twenty cleared facility locations. Honesty about fit is part of the operating model.

Public profiles and sameAs

• Kaggle — bopengiowa (Top 200 overall)
• GitHub — profile maintained; handle available on request. Sample repositories shared during technical interviews.
• Email — [email protected]