Data footprint
Large-scale unstructured data preparation: cleaning, transformation, and pipeline work that turns raw records into a usable training and evaluation surface.
Deep dive
Platform delivery, data preparation, and annotation automation.
The delivery layer behind model work: Databricks workflows, employee-facing RAG, data preparation, annotation and synthetic-data throughput, experiment iteration, and maintainable code.
Strong model work depends on the layer behind it: data, tooling, governed release controls, team coordination, and repeatable iteration.
Platform layer
Employee-facing retrieval assistant on governed platform services, with answer quality raised through HyDE, multi-query retrieval, and reranking, then checked against human-verified evaluation sets.
How the work runs
Release gates, audit logging, token/cost tracking, annotation automation, synthetic coverage, refactoring, and team coordination move together.
Platform map
From raw records to repeatable iteration.
The path moves through intake, preparation, annotation or augmentation, model iteration, and operational feedback.
01
Data intake
Work starts with large structured and unstructured sources, not clean benchmark inputs.
02
Preparation and ETL
Cleaning, transformation, Databricks-backed handling, and release controls create a usable training and evaluation surface.
03
Annotation and coverage
Automation and synthetic data expansion help reduce manual-label bottlenecks in class-heavy NLP work.
04
Model iteration
Transformers, LLMs, RAG, and supporting experiments move faster when the data layer advances with them.
05
Delivery loop
Refactoring, mentoring, token/cost tracking, and small-team coordination keep the work maintainable.
Delivery layers
The layers around the model work.
Databricks workflows
Platform choices shape iteration speed.
Recent delivery pairs LLM and NLP work with Azure Databricks so large-scale data handling and experimentation can move inside the same operating surface.
Data preparation
Useful modeling starts with careful data work.
Preparing unstructured records at scale, then replacing per-experiment setup with config-driven preprocessing and embedding pipelines, shows depth beyond clean benchmark data.
Annotation automation
Coverage has to scale or the project stalls.
Automated collection, annotation support, and synthetic-data expansion reduced labeling pressure in a many-class NLP problem and kept experimentation moving.
Mentoring
Delivery credibility includes code and people.
Refactoring Python code, mentoring junior data scientists, and managing 4 direct-report data science interns at Blue Guardian helped turn model work into a maintained delivery path.
Results
What the delivery layer produced.
Data preparation
Unstructured data at scale
Prepared for generative chemistry model work at Servier Canada.
Employee RAG
Measured retrieval quality
Employee-facing retrieval assistant on governed platform services, scored with human-verified evaluation sets and LLM-as-judge, then piloted with support staff.
Model preparation
Config-driven preparation
Preprocessing and embedding pipelines moved into configuration, so a new model run starts from a prepared surface instead of a fresh script.
Annotation cost
Manual labeling displaced
LLM-based data generation, validation, and automated annotation absorbed labeling work that would otherwise have been done by hand.
Coverage expansion
LLM-generated synthetic text
Used to expand class coverage and accelerate iteration in multi-class mental-health text classification.
Forms workflow POC
Evaluated against historical cases
Report-drafting agents with human review, aimed at the rework loop that starts when a form comes back for modification, and evaluated end to end on historical cases rather than a demo.
Agentic maintenance
Failing runs triaged automatically
An agent harness diagnoses a failed pipeline run, validates the fix on sandboxed data samples, and stages a deploy-ready change for human approval. Later extended to automated code review.
Claim-duration model
Simplest model that met the bar
A gradient-boosted claim-duration classifier chosen over transformer alternatives after benchmarking accuracy, latency, and cost, then kept under drift monitoring with retraining alerts.
What makes it real
Working patterns behind the results.
01
The data path is maintained, not treated as a one-time preprocessing script.
02
Automation is used to reduce bottlenecks, not to hide weak workflow ownership.
03
Leadership shows up as iteration speed, cleaner code, and team throughput.
The practical evidence is in the data preparation, platform decisions, automation strategy, and delivery loop that keep experimentation grounded.