Accepting new engagements
We review and build forecasting, backtesting, and risk models that can be reproduced, challenged, and explained when a decision depends on them.
01section What we solve
Most engagements start with a model or result someone needs to trust before they act on it.
The numbers are strong, but you cannot tell whether the result is signal or future information leaked into the test.
We reproduce the backtest from point-in-time data and trace every input before capital is at risk.
The model worked in one regime and quietly fails when volatility, correlations, or market structure change.
We stress the model across regimes so you know how it behaves when conditions turn.
The model drives decisions, but assumptions, validation, and limitations are not documented in a form reviewers can inspect.
We produce methodology, validation, limitations, and reproducible code so the reasoning travels with the result.
The forecast is a point estimate with no honest view of how wrong it could be.
We deliver calibrated uncertainty, validated out-of-sample, so decisions account for a range of outcomes.
02section Engagements
Choose the engagement that matches the decision in front of your team. Scope, fee, timeline, and deliverables are agreed in writing before work starts.
E.01
An outside technical review of a strategy or model before you size it up: leakage, fragile assumptions, and results that will not reproduce.
OutcomeYou learn whether the result is real before capital depends on it, with a documented technical findings memo you can put in front of an IC or a risk committee.
Learn more: model validation Learn more: backtest review Scope this engagement
E.02
A forecasting or risk model built from your data, validated out-of-sample, and delivered with uncertainty stated, not hidden.
OutcomeYou get a model whose behavior you understand and can explain, including where it is weak, instead of a black box you have to take on faith.
E.03
A review of the pipelines, data flow, and deployment around your models so the system is reproducible, monitored, and ready for audit.
OutcomeYou move from a model that works on someone's machine to a documented, reproducible, monitored system you can defend to oversight.
E.04
Practitioner-led training for your team, built around your stack and the problems you actually face, not a generic course.
OutcomeYour team leaves able to spot the mistakes that sink models and build or review work to a higher standard on their own.
03section Process
Four steps, with a deliverable at each one. You always know what you are getting and when.
We define the problem, the data you have, the decision it feeds, and what defensible has to mean for your IC, risk committee, or technical reviewer. We agree on scope, constraints, and success criteria before any work begins.
You receiveA written scope and engagement plan: objectives, data and access requirements, milestones, and a fixed or scoped fee.
We examine what exists: the model, the backtest, the assumptions, or the research the build depends on. We surface failure modes early: leakage, look-ahead, regime sensitivity, and the gap between in-sample fit and a live decision.
You receiveA findings memo covering what holds, what does not, the methods we tested, and a ranked list of issues with recommended fixes.
We implement the model, pipeline, or fix in code, with validation built in rather than added after. Every result is reproducible from data lineage to output, and reviewed against the criteria set in scoping.
You receiveReproducible code and notebooks, a validation report, and the model or system itself, documented for review.
We hand the work to your team so it runs without us, walking through the code, the assumptions, the monitoring, and the limits. The goal is that your people can defend, maintain, and extend it.
You receiveHandoff documentation, a walkthrough session, and where applicable a monitoring plan and the artifacts needed for governance or audit review.
04section Proof
Every engagement is built to be inspected: reproducible notebooks, methodology memos, validation reports, and handoff documentation. Where client work is confidential, we show the standard through redacted examples and synthetic-data artifacts.
What every engagement produces
Code and notebooks that regenerate every result from raw data to output, with no manual steps or unexplained numbers.
A written account of the methods, assumptions, and choices behind the work, so the reasoning travels with the result.
Out-of-sample tests, leakage and look-ahead checks, and known limitations, written for a reviewer and not just for us.
The setup, structure, and operating notes your team needs to run, maintain, and extend the work without us.
Where a model goes to production: what to watch, what should trigger review, and how to tell when it has drifted. Provided where applicable.
Representative engagements
Model & backtest audit. A quant or PM team about to allocate to a strategy whose backtest looks strong. Outside technical review of the strategy's data handling, backtest construction, and assumptions, checking for leakage, look-ahead, survivorship, and overfitting before capital is committed.
Forecasting & risk model build. An investment or fintech team that needs forecasts with honest uncertainty around them. Building a probabilistic forecast, scenario design, or VaR / Expected Shortfall model, with validation and documentation built for review.
ML infrastructure review. A data-driven team whose models work in research but are hard to trust in production. Review of the pipeline that feeds and serves models across lineage, features, evaluation, deployment, and monitoring to find where results can silently degrade.
See public synthetic-data artifacts that show the deliverable standard without exposing client information. Engagement details and references are held under NDA; we do not publish client names, data, or results without written permission.
Read a sample findings memo Open the review standard Download the synthetic notebook
05section Education
We teach the same econometrics, statistics, and machine learning we apply in engagements: math-first where it matters, hands-on throughout. The work runs through the Private Quant Training Cohort; the areas below are the ground it covers.
06section AI & Data Engineering
The infrastructure that carries research into production: data pipelines, feature stores, and the deployment, monitoring, and evaluation that keep models accountable. Engineered for reproducibility and audit, with on-chain data and settlement only where a client genuinely needs it.
Streaming and batch pipelines, feature stores, and reproducible training and deployment. Versioned data and models, evaluation gates, and monitoring keep production tied to what was validated.
Model integration, retrieval, and evaluation pipelines built for production use, measured against task-specific metrics, monitored in operation, and accountable when they drift.
For clients who need it: ingestion and analysis of on-chain data, and security-first smart-contract work with auditable, upgrade-aware patterns. Offered on request, not as a headline.
07section Built by StatGazer
Alongside client work, StatGazer builds small software products where privacy, clarity, and long-term use matter more than attention mechanics.
iPhone app
GoodUp is a private iPhone app for keeping a quiet rhythm of giving, care, promises kept, and weekly reflection.
Product principle
The app keeps its surface calm: no public profile, no leaderboard, and no streak pressure around acts of care.
Learn about GoodUp08section Founder
Evgenii Azarov
Data Scientist · Financial Engineer · Educator
StatGazer was founded by Evgenii Azarov, a data scientist and financial engineer with a PhD in Law, finance training at NYU, and 20 years of private teaching and consulting for family offices, investment teams, and researchers. The firm is intentionally founder-led: scoping, technical review, model work, and handoff are handled directly rather than passed through a bench of junior staff. Public synthetic artifacts are available below so prospective clients can inspect the working standard before sharing confidential data. See the founder page for the full credibility ledger.
Reach the founder directly at hello@statgazer.com.
Entity
StatGazer LLC · Registered in New York, United States · Operating globally.
Vendor / KYC
Use hello@statgazer.com for onboarding, NDA routing, tax forms, and procurement details. E&O coverage is addressed during onboarding; raise specific requirements before paid work begins.
Data handling
NDA-first, minimal extracts where possible, encrypted provider infrastructure, founder-only access unless agreed otherwise, and no third-party AI training.
Professional boundary
Technical model review, validation, research, and engineering consulting — not financial-statement audit, regulatory assurance, investment, legal, or tax advice.
09section FAQ
Every engagement starts with a scoping call, then a written scope and fee before any work begins. From there we run four steps: scoping, review or research, build, and handoff. You receive a deliverable at each one. Most engagements are scoped as a fixed piece of work; longer or embedded research runs on an agreed cadence.
Yes. We sign an NDA before reviewing confidential data, and we are glad to work under yours. Where possible we work inside your environment or on a minimal extract, retain only what the engagement needs, and return or delete data on request at the end. Access is limited to the founder unless otherwise agreed in writing. We do not use client data for third-party AI training.
No. StatGazer provides technical model review, validation, research, and engineering consulting. It is not a CPA firm, public accounting firm, broker-dealer, investment adviser, or regulatory assurance provider, and its deliverables are not financial-statement audit opinions or regulatory assurance reports.
You own the deliverables produced for your engagement: the code, models, notebooks, and reports. We retain our pre-existing methods, tooling, and general know-how. Specific ownership and license terms are set in the engagement agreement so there is no ambiguity later.
Pricing is scoped per engagement. After the scoping call we send a written scope with a fixed fee where the work is well-defined, or a clear rate and estimate where it is open-ended. We do not quote a price before we understand the problem, and we do not bill for work outside an agreed scope without agreeing it first.
We reply to enquiries within 24 hours. Turnaround on the work itself depends on scope and is agreed in writing before we start; we would rather commit to a date we can hold than to one that sounds fast.
An outside reviewer gives the internal champion a defensible second read: leakage checks, reproducibility, assumptions, and limitations documented by someone who did not build the original model. Your team keeps ownership and context; StatGazer adds a technical review record that can travel to an IC, risk committee, or vendor review.
Use internal MRM when your organization needs ownership, policy enforcement, and recurring controls. Use a Big Four or regulated assurance provider when procurement requires a large assurance firm, attestation, or a formal enterprise program. Use StatGazer when the gap is focused technical evidence: reproduction, leakage checks, validation design, and a findings memo your team can inspect.
Engagements are scoped in increments, with written artifacts delivered as work progresses. The client owns the deliverables produced for the engagement. If founder availability changes materially, we pause new work, hand over completed artifacts, and agree the next step in writing rather than silently substituting junior delivery.
That can be a useful outcome. The memo still documents what was checked, what evidence supported the result, what limitations remain, and what monitoring or follow-up is sensible. We do not manufacture findings to justify the engagement.
For scheduled training, you can cancel or reschedule up to ten business days before the start date for a full refund or credit; after that, fees may be partly retained to cover reserved time. For self-paced access, terms are stated at the point of purchase. Full terms are in the Refund Policy.
10section Contact
Bring us a model, a backtest, or a forecasting problem you need to defend. We will tell you the shortest honest path to a result you can stand behind and whether we are the right team for it.
We reply within 24 hours · Direct line hello@statgazer.com
What happens on the scoping call
Training & courses
A separate track from consulting. If you want a paid program such as the training cohort, request access and we will send current terms before enrollment.
Request program access
