Signal Extraction from Noise

The practice: privacy-preserving architectures; multi-model orchestration; precision/recall trade-offs mapped to business costs; compliance-aware AI in regulated environments. The outcome: products that work in production, not just in demos.

I'm Tatiana - an AI Product Manager who ships ML systems to production, optimizes models for business constraints, and makes AI behave predictably in high-stakes environments.

  • Current: 500h Sorbonne-accredited Data Science certification, fraud detection models, case studies.
  • Previously: 6.5 years at Native (AI-powered B2B platform) and Emplifi (SaaS analytics).
  • Based in: Leipzig (UTC+1)
  • Fluent: English (C2) · German (C1) · Russian (C2)

⟶ Download full CV (PDF) - updated December 2025

. . .

Open To

  • AI PM roles (full-time, hybrid/remote in EU)
  • Fractional product leadership for 0→1 AI features
  • Consulting on responsible AI, model evaluation, EU AI Act

Best fit: Teams building real AI/ML (not wrappers). Care about privacy, compliance, and explainability. Need someone technical enough to evaluate models and strategic enough to ship.

. . .

Down the rabbit hole ⇣ ◎

·
“Drink Me” — Who I Am

I operate where product strategy meets data science. My job is to turn probabilistic systems into predictable user experiences. That means insisting on measurement, designing for privacy by default, and aligning model decisions with business risk.

“The White Rabbit” — Current Work
  • Completingadvanced ML training (ensembles, hyper-parameter tuning, MLOps fundamentals).
  • Buildingproduction-style models and publishing case studies here.
  • Exploringfractional product leadership & responsible-AI advisory.
  • Available forAI PM roles, contract work, strategic consulting (EU-based, remote/hybrid).
“Cheshire Cat” — The Approach
Context-aware ML orchestration
Not one model, the right model for the context (formality, language pair, latency/cost/accuracy constraints).
Privacy-preserving architecture
PII detection & masking pre-inference; re-injection post-translation; zero leaks in production.
Model evaluation as product thinking
Precision vs recall drives fraud costs vs false-positive pain; ROC/PR curves inform thresholds; SHAP audits what the model actually learned.
Rapid iteration under constraints
MVPs in <30 days for FedRAMP-adjacent contexts; A/B threshold tests; feature-flag rollouts.
“Mad Hatter’s Table” — What’s Been Shipped
  • Multi-engine translation platform4 commercial ML providers orchestrated, 100+ languages, ~40% fewer manual overrides.
  • Privacy-preserving ML pipelinereal-time entity masking, <500 ms latency, zero third-party PII exposure.
  • Product-led growth systems20× activation via Stripe automation & self-serve; 25% support-load reduction.
  • High-trust client enablementU.S. Air Force & SBIR orgs; GDPR/ISO/FedRAMP-adjacent compliance.
  • End-to-end MLinsurance fraud detection (EDA → FE → SMOTE → threshold tuning → Streamlit).
  • This platformVanilla JS/HTML/CSS, GitHub Actions CI/CD, custom components.
“Looking-Glass Toolkit” — Technical Foundation
500h Sorbonne-accredited DS certification
(completion Dec 2025): evaluation, precision/recall optimization, FE, interpretability (SHAP), ensembles, imbalance handling, deployment.
3.5 years AI/ML product management
B2B SaaS: conversational AI, NLP feature design, multi-model orchestration, real-time inference.
Hands-on stack
Python, scikit-learn, XGBoost, pandas, SQL, PostHog, Mixpanel, Streamlit, Docker basics, GitHub Actions, REST APIs, Figma.
Domain expertise
Privacy-preserving ML, entity recognition, model monitoring, anomaly detection, A/B testing, EU AI Act thinking.
“Queen of Hearts” — Philosophy in Practice
  • Signal over noisecut what doesn’t matter; clarity compounds.
  • Structure over chaospredictable systems, unsurprising UX, unambiguous requirements.
  • Intention over accidentthresholds and decisions have real user costs.
  • Execution over strategyMVPs beat perfect plans.
  • Ethics over velocityoptimize without harming; responsible AI as structure, not theater.
“Wonderland Use-Cases” — What This Enables
AI/ML teams
Bridge DS ↔ product; write technical PRDs; debug precision/recall trade-offs; explain decisions to non-technical stakeholders.
0→1 launches
Ship in weeks under GDPR/ISO/FedRAMP-adjacent constraints; structure ambiguity into executable plans.
Regulated environments
Privacy patterns, EU AI Act compliance thinking, zero-incident track record.
Cross-functional leadership
Coordinate distributed eng/ML/design; maintain velocity without sacrificing quality.

A final note

The universe trends toward disorder. Products are pockets of order. Good AI is predictable behavior carved from probabilistic chaos. Low entropy is the practice of building systems that work — and don’t make the world worse. That’s the work.