Direct technical context
The same person who scopes the question reads the model evidence, traces the data, writes the findings, and handles the technical handoff.
Founder · Direct delivery
Evgenii Azarov.
StatGazer is founder-led by Evgenii Azarov, PhD in Law, Russia (2012), with finance training at NYU and 20 years of private teaching and consulting for family offices, investment teams, and researchers. His work sits at the intersection of econometrics, statistics, machine learning, financial engineering, and production software.
Why founder-led
No bench handoff between the sale and the work.
For model validation, backtest review, and quant research, the expensive mistakes often live in details: timestamp alignment, undocumented assumptions, fragile validation, unreproducible notebooks, and ambiguous model purpose. Those details are easy to lose when the person selling the engagement is not the person doing the review.
Senior attention by default
StatGazer is intentionally small. The model is not a large team selling junior leverage; it is focused review, research, and engineering for problems where quality matters more than headcount.
Clear limits
When a request is outside StatGazer’s role, the firm says so. The service is technical consulting, not legal advice, tax advice, investment advice, or regulatory assurance.
Working standard
Evidence your team can inspect.
Prospective clients should not have to trust adjectives. The standard is practical: a model review should say what was checked, what failed, what held up, what could not be verified, and what should be fixed first.
What the work emphasizes
- Point-in-time correctness, leakage checks, survivorship, and data lineage.
- Validation design, out-of-sample behavior, calibration, and model limitations.
- Reproducible notebooks, code paths, and documentation that travel with the result.
- Written findings that distinguish facts, assumptions, unresolved risks, and recommendations.
Public proof artifacts
The site includes a synthetic sample findings memo and public synthetic notebook reference so buyers can inspect the format without sharing client data. Synthetic artifacts are not a track record or client case study; they show the standard of documentation, reproducibility, and technical framing.
Company facts
Built for vendor review before confidential work starts.
Institutional buyers often need basic facts before a scope is approved: entity, jurisdiction, contact route, data handling, and the boundary of the service. StatGazer keeps those facts visible rather than hiding them behind a sales call.
Entity and contact
- Legal entity: StatGazer LLC, a New York limited liability company.
- Operating base: New York, United States; operating globally.
- Direct contact and vendor-onboarding route: hello@statgazer.com.
- Primary public profiles: LinkedIn and GitHub.
- E&O insurance: handled as a procurement requirement. If your vendor process requires specific coverage, raise it before paid work begins.
Data handling
Engagements are NDA-first. Where possible, StatGazer works inside the client environment or on the smallest useful extract. Access is founder-only unless otherwise agreed in writing. Client data is used only for the engagement and is not used for third-party AI training.
Governance vocabulary
Disciplined review without overclaiming assurance.
Model-risk frameworks such as SR 11-7 are useful because they name practices that also matter outside banking: independent challenge, conceptual soundness, validation evidence, limitations, monitoring, and documentation. StatGazer uses that as a practical parallel, not as a claim to provide bank regulatory assurance or any official certification.
Professional boundary. Technical model review, validation, research, and engineering consulting — not financial-statement audit, regulatory assurance, investment, legal, or tax advice.
How buyers should evaluate the firm
Ask for evidence before sending sensitive data.
A prudent buyer should verify fit before sharing confidential files. Start with the public site, the sample memo, the synthetic notebook reference, the LinkedIn profile, the GitHub profile, and a scoped call. If the problem needs a different credential, jurisdiction, assurance provider, or team size, that should be clear before procurement work begins.
This is also why the first conversation is a scoping call rather than a sales demo. The useful question is whether StatGazer can create a credible technical review record for your specific decision, under the confidentiality and access constraints your organization requires.
What to verify
Confirm the entity, contact route, public profiles, scope boundary, data handling terms, and whether the engagement will be founder-delivered. Those are the facts that matter before NDA and access.
What to ask on the call
Ask how the review would be scoped, which artifacts are required, what would be considered out of scope, and how findings would be written for both technical and decision audiences.
What not to infer
Do not infer audit assurance, investment advice, legal advice, tax advice, or guaranteed model performance. The role is independent technical review, research, engineering, and documentation.
Next step
Start with the technical question.
Use the consultation form for high-level context only. If there is a fit, the next step is a short scoping call and a written scope before any confidential data or paid work begins.