Data Scientist specializing in analytics, predictive modeling, machine learning, business intelligence, and data-driven executive decision making. Proven history delivering +$18.4M in ARR across Netflix, Uber, and Airbnb.
Data science without commercial translation is just expensive compute. My work centers on causal clarity, business value realization, and robust production engineering.
With over 9 years of hands-on experience across Silicon Valley leaders (Netflix, Uber, Airbnb) and venture-backed FinTech unicorns, I operate at the intersection of mathematical rigor, data engineering architecture, and C-suite commercial strategy.
I specialize in moving beyond naive correlation to build production-grade causal inference pipelines, dynamic pricing engines, uplift models, and automated business intelligence cockpits. My models have directly generated over $18.4M in preserved ARR and operational cost efficiencies.
Rooted in probability theory, mathematical statistics, and scalable computer science at world-renowned research institutions.
Specialized in Bayesian non-parametric causal inference, observational study adjustments, and high-dimensional regularization. Advised in computational statistics research labs.
Focus on stochastic optimization, numerical linear algebra, Markov Chain Monte Carlo (MCMC), and large-scale distributed graph computing.
Graduated Summa Cum Laude with Highest Departmental Honors. Tau Beta Pi Engineering Honor Society.
Comprehensive capabilities spanning the entire data lifecycle: from raw distributed ingestion to causal inference and executive synthesis.
Battle-tested toolchain spanning statistical programming, enterprise data warehouses, cloud infrastructure, and modern data platforms.
Real-world case studies detailing business problem framing, massive dataset ingestion, statistical modeling, insights, and quantifiable financial impact.
Engineered an end-to-end churn risk prediction pipeline with XGBoost, SHAP causal interpretability, and automated proactive retention playbooks.
Multi-horizon demand forecasting model reconciling SKU-level predictions up to country-level financial guidance using DeepAR and MinT.
Dual-encoder candidate generation and contextual ranking microservice serving real-time personalized media recommendations.
Sub-second anomaly detection graph combining structural graph neural network embeddings with streaming gradient boosting.
Unified attribution engine combining Bayesian MMM (Markov Chain Monte Carlo) with randomized geo-lift experimentation post-iOS ATT.
Analytical data lakehouse on ClickHouse and dbt enabling real-time retention and funnel queries for product teams without SQL queues.
Monotonic gradient-boosted underwriting scorecard maximizing predictive power while strictly adhering to ECOA regulatory compliance.
Clinical decision-support dashboard predicting 30-day unplanned readmission probability at point-of-discharge with EHR integration.
Explore interactive visual mockups across core operational functions. Switch categories and time ranges to simulate dynamic KPI re-aggregation.
How world-class data science bridges messy telemetry and multi-million dollar boardroom strategy in 7 systematic phases.
Interactively test classification decision thresholds. Watch the Confusion Matrix, Precision, Recall, and F1 Score recalculate dynamically in real time.
Peer-reviewed conference publications, technical whitepapers, and industry case studies contributing to modern statistical methodology.
Proposes a penalized Bayesian structural time series framework to resolve spatial network interference during hyper-local A/B rollouts in urban ride-hailing networks.
Demonstrates how two-model and X-learner causal estimators prevent subsidizing "sure things" in churn prevention campaigns, saving 34% in wasted discount spend.
Formulates quadratic optimization to guarantee that SKU-level supply predictions sum exactly to regional financial forecasts while preserving marginal volatility bounds.
A blueprint for data engineering teams transitioning from sluggish overnight batch runs to sub-second streaming lakehouses with automated data contract enforcement.
Real metrics delivered across production systems. Every analytical model must tie directly to revenue expansion, cost reduction, or risk mitigation.
Formally verified proficiencies across leading cloud data providers, business intelligence vendors, and statistical institutes.
Leading high-performing data science, analytics engineering, and business intelligence teams in hyper-growth tech environments.
Perspectives from engineering executives, business leaders, and academic mentors on collaboration and commercial execution.
"Alex possesses that rare combination of elite mathematical depth and executive clarity. At Netflix, his churn early-warning models transformed how our retention teams operate, directly preserving millions in ARR. He makes complex Bayesian models intuitively clear to the C-suite."
"At Uber Marketplace, Alex engineered spatial dynamic pricing algorithms under extreme sub-15 millisecond latency constraints. His systems drove measurable efficiency gains in driver hours and passenger wait times across 40 cities without sacrificing stability."
"During his doctoral work at Stanford, Alex established foundational contributions to causal inference under high-dimensional unobserved confounding. He solves the hardest statistical challenges with remarkable engineering pragmatism."
Swipe or scroll through recent technical essays, production architectures, and decision science methodologies published by Dr. Alex Vance on LinkedIn.
In-depth technical guides, statistical methodologies, and executive strategic essays on modern analytics architecture.
When driver and rider behavior interacts in real time, classical SUTVA assumptions break. Here is how we implemented cluster randomization and synthetic controls at scale.
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A deep dive into Minimum Trace (MinT) and optimal combination matrix algebra to ensure supply chain replenishment matches GAAP revenue projections.
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How we eliminated Python Global Interpreter Lock (GIL) bottlenecks, vectorized model features with SIMD, and deployed dual-tower rankers on Triton Inference Server.
Read Full Essay →Available for Principal Data Science roles, Executive Advisory, Machine Learning architecture reviews, and high-impact analytics consulting.