Walter Ullon

Polly.io  ·  Remote

• Led end-to-end modernization of the analytics platform, migrating 16 production pipelines from legacy batch ETL to Delta Live Tables streaming with Change Data Capture: cutting latency from 90 to 22 minutes, improving data freshness from 2-hour batch windows to 15-minute incremental CDC, and eliminating hundreds of millions of duplicate records across 10B+ daily rows. Designed a 6-level dependency graph across dual orchestration jobs with cadence-decoupled core and reporting tracks.
• Architected a five-module production ML framework for loan volume forecasting: a period-truth computation pipeline (incremental, windowed, and full-overwrite modes), a Unity Catalog Feature Store with lag, rolling, year-over-year, and holiday-effect features with leakage-controlled windowing, Optuna-tuned AutoML across nine model families (XGBoost, LightGBM, CatBoost, OLS, Ridge, Lasso, ElasticNet, ARIMA, ETS) with time-ordered cross-validation and MLflow champion selection, seeded multi-step inference for production-horizon forecasting, and row/batch monitoring with truth-lag handling. Documented in a 63-page TDD.
• Drove six-figure annualized infrastructure cost savings through a Databricks compute audit: DLT cadence optimization (21% reduction, measured Day-1), zombie cluster elimination, and Dev Cluster rightsizing (97% cost reduction).
• Designed and deployed a production capital markets analytics application on Databricks Apps: per-chart LLM interpretations grounded in domain-specific context, streaming AI sidebar chat, AI-generated PDF reporting, and SQL injection prevention via allowlist pattern with OAuth and PAT dual authentication.
• Built a tiered loan status normalization system routing through SQL lookups and cross-client consensus before invoking LLM inference, achieving near-zero per-classification cost at production scale. Improved classification accuracy by enriching LLM prompts with sequential loan history context derived via SQL window functions.
• Applied queueing-theoretic steady-state analysis and Bayesian inference to mortgage pipeline pull-through modeling. Implemented a Dirichlet-Categorical transition model (PyMC, ~400K observed transitions) with conjugate posteriors and credible intervals for sparse-event transitions, enabling confidence-aware path probability estimation and operational bottleneck identification.
• Built an automated analytics knowledge graph spanning 29 production pipelines, enabling instant blast radius analysis, AI-agent dependency lookups, and auto-publishing 1,240+ field definitions to Confluence. Operationalized as a Claude Code custom skill providing a conversational pipeline navigator with authoritative lineage lookups across the full pipeline surface.
• Architected a pull-based Delta Sharing platform enabling external financial clients to access data on demand through recipient-property row filtering and layered security, eliminating file-push workflows entirely. Onboarded 3 external financial clients with zero manual DDL, self-service configuration, and Open Sharing protocol compatibility.

EZOPS  ·  New York, NY

• Owned the data science product roadmap end-to-end, arbitrating between engineering capacity, data science priorities, and client-facing demands across multiple concurrent platform tracks.
• Led structured discovery cycles and OKR planning that determined investment sequencing across model monitoring, data quality, and reconciliation automation initiatives.
• Defined success metrics and tracking frameworks for DS product launches, enabling data-driven go/no-go decisions on platform releases.
• Served as primary liaison between sales, client success, and data science engineering, translating client requirements into scoped technical specs and managing scope risk on delivery commitments.

EZOPS  ·  New York, NY

• Built supervised ML models for client record deduplication, anomaly detection, and behavior prediction across financial services data.
• Developed time-series forecasting pipelines and statistical reporting infrastructure used across production reconciliation workflows.
• Implemented experiment tracking, model versioning, and champion/challenger evaluation frameworks using MLflow and custom tooling.
• Built NLP pipelines for document classification and entity extraction on unstructured financial data.
• Designed and maintained data pipelines in PySpark and SQL on Databricks, supporting medallion architecture and Delta Lake migration.

ML & Data Science

Data Platform

AI Tooling

Software Engineering

Montclair State University  ·  Montclair, NJ

• Top Graduating Senior in the Department of Mathematics.
• Concentration in Applied Mathematics. Research in Population Dynamics and Epidemiology.
• Published: "Early Warning Signals for Epidemic Extinction" — International Journal of Chaos and Dynamics.