In progress
Master of Business Administration (MBA)
Business Analytics (STEM) · Pace University
Cover sheet · New York, NY
Data Engineer & Business Intelligence Analyst | AI-enabled data systems
4+ years solving ambiguous business problems in technology, healthcare, energy, finance, product and operations. I pair quantitative modeling and SQL/Python analytics with cross-functional work, so recommendations get adopted and dashboards, forecasts and AI-enabled tools keep running.
$120K
annualized savings at PSEG
6h → 5 min
fleet-planning processing time at PSEG
$200K+
client value delivered at ASH-TECH
30%
reduced onboarding time at WebScape
01 · About
I start with open-ended business questions, define the KPIs, model the scenarios and turn the results into tools and recommendations leaders actually use.
Today I own analytics workstreams at Natera, including Voice of Patient, AI Disposition and Twilio/Calabrio reconciliation. I also lead a modernization workstream for a mission-critical gas-electric incident notification system at Consolidated Edison.
Earlier, at PSEG and ASH-TECH Solutions, I built forecasting tools and financial models that turned ambiguous operational and client questions into decisions.
Outside work I build LLM agents, ML infrastructure and evaluation tooling. My published research includes GRAFT, a gRPC-routed agent framework (ACM AI and Agentic Systems, 2026), and Branch-Commit-Validate for autonomous red-team agents (Springer SAM, 2025).
02 · Experience
Oct 2026 – Present
New York, NY (Hybrid)
In progress
Owns a cross-functional modernization workstream for a mission-critical gas-electric incident notification system, from requirements through validation and delivery.
Jun 2026 – Present
San Carlos, CA (Remote)
In progress
Owns strategic analytics workstreams and delivers recommendations used by Operations, Product, Finance, Sales, Support and leadership.
Jun 2025 – May 2026
Edison, New Jersey
In progress
Solved an ambiguous fleet-planning problem with a custom forecasting tool and led continuous-improvement workstreams across operations.
Jul 2023 – Aug 2024
Gujarat, India
Led client-facing analytics and strategy engagements and managed BI and data workstreams from requirements through adoption.
Jul 2021 – Jun 2023
Mumbai, India
Partnered with clients to diagnose business and operational issues and owned several delivery workstreams at once.
03 · Projects
Each project is drawn as the thing it does: a pipeline, a gate, a graph, a log. Thirteen projects, from forecasting to supply-chain security.
LLM agents
A tool-calling travel agent on the Anthropic API that searches flights and hotels, checks weather and builds day-by-day itineraries. It runs as a CLI chat or a FastAPI endpoint with multi-turn conversations.
Each turn is capped at 8 model calls and a 60-second deadline, and the agent returns an honest fallback instead of fabricated results.
RL evaluation
An RL environment in which an LLM agent trains a Process Reward Model end-to-end. The model is graded against an Outcome Reward Model baseline and a PRM800K-style reference on a held-out MATH slice.
The real PRM scores 0.770 in the demo, while the reward-hacking submissions score 0.000.
NLP tooling
A local-first job-hunting assistant that discovers jobs, parses descriptions with spaCy, scores resume fit transparently and drafts tailored resumes and outreach. It never auto-applies or auto-sends.
Uses a transparent 7-component estimated match score, and every outreach draft requires manual review.
Forecasting
Hierarchically reconciled energy demand forecasts using MinT shrinkage reconciliation, scored on pinball loss. Regional forecasts sum exactly to the national figure and the upper tail stays accurate.
Independent forecasts disagreed by up to 612 MW; after reconciliation the gap is float round-off.
MLOps
Progressive delivery for ML models: a FastAPI gateway serves ONNX models in shadow and then canary. Sequential tests decide automatic promotion or rollback.
Moves from a shadow stage to canary stages (e.g. 10% then 50%) with sticky salted-hash routing.
ML pipelines
A content-addressed, parallel pipeline runner for reproducible ML workflows. It hashes code, params and data, explains why a stage is stale and traces artifact lineage.
SHA-256 cache keys cover code, params, input content and env vars, and AST normalization ignores comments.
Agent infrastructure
A single-node, Temporal-style durable execution runtime for LLM agents. Event-sourced SQLite history and deterministic replay let a workflow resume after a crash without re-running finished steps.
Every state change is one lease-fenced SQLite transaction, with zero runtime dependencies.
ML security
A static scanner for ML model files that inspects pickle, PyTorch, Keras, safetensors, GGUF and ONNX without deserializing them. It reports findings as text, JSON or SARIF.
Checks every import against an allow-list policy, with format-specific checks for documented attacks.
Supply-chain security
An offline detector for typosquats, dependency-confusion exposure and risky install hooks in npm and PyPI dependencies. It outputs SARIF for CI.
Compares names against a snapshot of about 12,000 top PyPI projects and 14,000 high-impact npm packages.
Detection rules
A Sigma detection-rule compiler that parses rules into a typed expression tree and renders Splunk SPL, Elastic EQL, Microsoft KQL and SQLite SQL, checked by property-based differential tests.
Each backend consumes the same expression tree, and the SQL output runs in SQLite to check the others.
Log parsing
A from-scratch Rust parser for Windows EVTX event logs with a chunk-parallel JSONL pipeline, CRC verification, damaged-file recovery and Sigma-style field mapping for detection work.
Matches the reference parser on all 4,805 records of 9 public sample logs.
Audit integrity
A hash-chained audit log that adds Merkle checkpoints with externally anchored roots.
Detects re-chained truncation that plain hash chaining silently accepts.
Rule quality
A CI harness for YARA rule quality that lints rules, proves they fire on fixtures, scans goodware for false positives, profiles scan cost and diffs rule versions.
Gates pull requests on behaviour, not just syntax, with JUnit and SARIF reports.
04 · Skills
The tools I reach for, grouped the way a build list is grouped.
05 · Education & research
In progress
Business Analytics (STEM) · Pace University
06 · Contact
Looking for someone who can turn messy data into forecasts, models and AI-enabled tools that teams rely on? Reach out by email or connect on LinkedIn.