VTN · FORECAST ACCURACY 91.9% | DATA ACCURACY 98% | RECOVERABLE SPEND $1.2M+ | REVENUE CAPTURE EUR500K/MO | ANNUAL SAVINGS $200K+ | GPA 3.62/4.0 | REPORTING EFFORT ↓40% | QUERY PERFORMANCE ↑30% | ERROR RATE ↓60% | AVAILABLE IMMEDIATELY | M.S. OREGON STATE · 2026       VTN · FORECAST ACCURACY 91.9% | DATA ACCURACY 98% | RECOVERABLE SPEND $1.2M+ | REVENUE CAPTURE EUR500K/MO | ANNUAL SAVINGS $200K+ | GPA 3.62/4.0 | REPORTING EFFORT ↓40% | QUERY PERFORMANCE ↑30% | ERROR RATE ↓60% | AVAILABLE IMMEDIATELY | M.S. OREGON STATE · 2026      
Financial Analyst · FP&A · Business Analyst · Data Analyst
M.S., Business (Financial Analytics) — Oregon State University

Vachan Thambi Naveen

I work at the intersection of financial analysis, data engineering, and business reporting. Two years at a global enterprise services company building forecasting models, reconciliation controls across SAP and Oracle EPM, and the Power BI dashboards finance and operations leadership actually ran on. M.S. in Business (Financial Analytics) from Oregon State, graduated June 2026 and available immediately.

↓ Download Résumé (PDF) View experience
Open to Work · Available Immediately
📍 Corvallis, OR · Open to relocation
🌍 F-1 OPT · STEM extension eligible
Vachan Thambi Naveen
Corvallis, OR · 2026
Experience
2+ yrs
Finance, Reporting & Analytics
Degree
M.S. Business
Financial Analytics · Oregon State · GPA 3.62
Work Authorization
F-1 OPT
STEM extension eligible · up to 3 yrs
Location
Corvallis, OR
Open to relocation · US-wide
§ 1.0

About Me

I'm a finance and analytics professional whose work sits across financial planning, reconciliation, and data engineering. At Colt Technology Services I built scenario forecasting models in Anaplan that hit 91.9% accuracy, ran monthly variance analysis across three business units on $500K–$1M in actuals, and reconciled forecast and consolidation data across Oracle FCCS and internal systems closely enough to surface a dormant vendor billing two years after deactivation, worth $1.2M+ in recoverable spend.

The engineering side is what makes the finance side faster. I write the SQL and Python that moves the data, then build the Power BI and Excel layers that finance and operations leadership make decisions on. Most recently I've been building multi-agent LLM systems for finance operations, with validation layers that stop unverified numbers from ever reaching a user.

Engineering degree first, then an M.S. in Business with a major in Financial Analytics at Oregon State (STEM-designated, GPA 3.62). I'm looking for roles in financial analysis, FP&A, business analytics, or data analytics where the work connects to real decisions and real numbers.

Quick Facts
Status● Open to Opportunities
AvailabilityImmediate
LocationCorvallis, OR
Work AuthF-1 OPT
DegreeM.S., Business
(Financial Analytics)
GPA3.62 / 4.0
Emailvachanthambi1999@gmail.com
Phone(541) 286-2789
LinkedInin/vachanthambi
GitHubvachanthambi
RésuméDownload PDF ↓
§ 2.0

Work Experience

Colt Technology Services
Analyst — Finance, Reporting & Analytics
Oct 2022 → Jul 2024
Global Enterprise Services
  • Led post-acquisition integration of 2M+ financial and customer records across SAP ERP and billing systems via automated SQL and Python reconciliation scripts, delivering 98% data accuracy and uninterrupted finance and revenue reporting through the cutover.
  • Reconciled forecast and consolidation data across Oracle FCCS (with PBCS exposure) and internal databases with audit-ready mapping and control documentation, uncovering a dormant vendor service billing two years post-deactivation and surfacing $1.2M+ in recoverable spend.
  • Developed scenario-based forecasting and long-range planning models in Anaplan, Python, and Excel using 3+ years of historical demand data, achieving 91.9% forecast accuracy and unlocking $200K+ in annual savings through better capacity and procurement timing.
  • Performed monthly variance analysis across 3 business units on $500K–$1M in revenue, gross margin, and operating cost actuals vs. forecast, delivering root-cause commentary to Finance leadership and BU heads 48 hours ahead of quarterly close.
  • Consolidated data from 5+ source systems into automated SQL and Python ETL pipelines, cutting manual reporting effort by 40% and enabling daily rather than weekly refresh of executive dashboards for Finance and Operations leadership.
  • Built Power BI KPI frameworks with custom DAX measures tracking revenue, gross margin, and service delivery, which became the standard pre- and post-billing cycle review tool and reduced analysis turnaround by 30%.
  • Designed SQL-based validation rules and reconciliation controls across billing and revenue reporting, reducing errors by 60% and strengthening monthly close and audit readiness ahead of quarterly review.
  • Refactored a shared SQL function library using CTEs, window functions, and indexing, standardizing reconciliation and operations workflows team-wide and cutting query execution time by 30% for 50+ users.
  • Identified inefficiencies in a client's analytical processes around integrating blockchain with existing database frameworks, leading implementation of a blockchain-based billing system that strengthened data integrity and increased monthly revenue capture by EUR 500,000.
  • Executed 20+ monthly UAT cycles across SAP and internally built billing and financial systems, logging 10+ defects over two years and driving them to closure with owning teams, then reconciling post-release billing and reporting outputs before production promotion.
  • Gathered reporting and system-change requirements directly from 2–3 stakeholders per request across Finance, Operations, IT, and client teams, confirming scope in writing and validating draft outputs with requesters before build; resolved 20–30 ad hoc analysis requests per month routed through JIRA from 5 cross-functional teams.
  • Partnered cross-functionally with Finance, Operations, and IT to deliver monthly executive review packs adopted by all 3 departments, and authored 4 SOPs covering reconciliation logic, Power BI refresh, and CRM workflows that cut new analyst onboarding time by two weeks.
Oregon State University
Student Assistant — McNary Student Center
Oct 2024 → Jun 2026
Concurrent with M.S.
  • Reconciled 200+ point-of-sale transactions totaling $1,500–$2,000 per shift at end of day in Excel, validating cash, credit, and alternate tender entries against system totals and delivering a verified daily close-out to the back office manager.
  • Rebuilt an inherited Excel reconciliation template, restructuring formulas and layout to cut daily close-out time by 40% and make the process easier for other student assistants to complete accurately, standardizing reporting across the team.
  • Supported 150–200 students per shift across 5 shifts weekly in a combined front-office and back-office role, handling customer relationship management, account and transaction inquiries, and escalations.
  • Trained and onboarded 5+ incoming student assistants on point-of-sale operations, end-of-day reconciliation, and front-desk service standards, keeping close-out coverage consistent across shifts.
Campus Hyre
Analyst Intern
Aug 2021 → Dec 2021
Early-stage startup
  • Built Python and SQL pipelines for multi-source data aggregation across operational and financial datasets, saving 15 hours of manual effort per week, improving processing efficiency by 35%, and informing pricing, resource planning, and capacity decisions.
  • Automated weekly Excel reporting workflows, replacing manual tracking processes and accelerating decision turnaround by 25%.
  • Built live Power BI dashboards to replace the manual Excel dashboards in use, rebuilding broken data sources and calculations, adding validation rules, and automating refresh, cutting errors per reporting cycle by 30% while applying descriptive statistics and trend analysis for business stakeholders.
§ 3.0

Selected Projects

5 agents · 2,866 txns · 3 systems · 0 unverified figures CRM ERP Billing ORCH Claude Reconciliation Variance Commentary SQL Guardrail NUMERIC VALIDATION LAYER retry · fallback to raw engine output
01 / AI SYSTEMS & FINANCE OPS

AI Finance Ops Copilot

A multi-agent Claude system that reconciles mismatched financial systems, decomposes budget-to-actual variance by driver, and refuses to let an unverified number reach the user.

  • 5-agent architecture (orchestrator + 4 tool-restricted specialists) reconciling 2,866 transactions across 3 heterogeneous PostgreSQL-modeled systems with no shared join key, resolving identity via RapidFuzz
  • Driver-based variance engine decomposing 24 months of modeled budget-to-actual data into volume, mix, price & FX, isolating a $2.7M currency offset hidden behind a +1.3% headline
  • Numeric validation over LLM commentary with retry and fallback: zero unverified figures across a 12-run evaluation harness; least-privilege read-only SQL role blocking writes and injection
PythonPostgreSQLClaude APIMulti-Agent SystemsSQLAlchemyRapidFuzzStreamlit
View on GitHub ↗
TV Y1 Y2 Y3 Y4 Y5 5-yr DCF · WACC/CAPM · Gordon Growth
02 / FINANCIAL MODELING

Automated DCF Valuation Analyzer

End-to-end tool ingesting any SEC 10-K PDF — LLM-powered extraction, a full DCF engine with WACC/CAPM, comparable company triangulation, and one-click Excel/PDF export from an interactive Streamlit dashboard.

  • Claude API + pdfplumber ingestion normalizing up to 40 pages of income statement, balance sheet & cash flow data from unstructured 10-K filings into a DCF-ready dataset
  • WACC via CAPM, 5-year FCF projections, Gordon Growth terminal value, validated through a 30-scenario sensitivity grid and cross-checked against 4 peers on EV/Revenue, EV/EBITDA & P/E
  • Applied end-to-end to Tiffany & Co.: intrinsic value per share landed within 96% of the price the company was actually acquired at. Prompt caching cut repeat-query API cost roughly 90%, under $0.05 per full valuation
PythonClaude APIStreamlitPlotlypdfplumberyfinanceDCFopenpyxl
View on GitHub ↗
positive 86.6% neutral complaint 11.9% K-Means PCA · 431,709 reviews · 8 clusters
03 / NLP & MACHINE LEARNING

Yelp Review Sentiment & Complaint Analysis

NLP pipeline on 431K+ Yelp reviews, converting unstructured customer voice into a quantifiable Business Health Score with revenue-impact estimates grounded in HBS research.

  • VADER + TF-IDF + spaCy lemmatization → 94% star-rating prediction accuracy with Logistic Regression
  • LDA topic modeling + elbow-optimized K-Means clustering surfaced 5 dominant complaint themes across 9,906 businesses, translated into targeted operational recommendations
  • Composite Health Score flagged 3,286 at-risk & 1,675 critical businesses; revenue opportunity estimates grounded in HBS research on Yelp rating elasticity
PythonVADERLDAscikit-learnNLPspaCyK-Means
View on GitHub ↗
Books 500 records PK · FK · Availability Members 200 · Fines max 5 books Transactions 2,000+ rows Borrow · Return Fines $171K modeled $0.50/day overdue Reservations 44% fulfilled Queue · 7-day expiry 8-table ERD · 10 SQL queries · Power BI
04 / DATABASE ENGINEERING & BI

Belly Library Management System

End-to-end database project — 8-table schema design, a Python/Faker synthetic data pipeline, 10 advanced SQL analytical queries, and a 3-page Power BI operational dashboard built on top.

  • 8-table relational schema with enforced foreign keys, business rule checks & 14 indexes, seeded with 200 members, 500 books and 2,000+ transactions via a Python/Faker generator
  • 10 analytical SQL queries using CTEs, RANK(), LAG(), rolling 3-month averages & conditional aggregation for overdue tracking, delinquency ranking and reservation fulfillment
  • 3-page Power BI dashboard (18 visuals, custom DAX) surfacing 61% availability, $171K outstanding fines and 44% reservation fulfillment across the generated dataset, as modeled decision drivers
SQLiteSQL/CTEsWindow FunctionsPythonPower BIDAXFaker
View on GitHub ↗
§ 4.0

Skills & Toolkit

Financial Analytics & Platforms
DCF & Valuation · WACC / CAPM
Forecasting & Long-Range Planning
Budgeting · Scenario Planning · Reforecast
Variance & Sensitivity Analysis
Monthly Close & Financial Reporting
P&L, Revenue, Cost & Gross Margin Analysis
Audit-Ready Documentation & Controls
Financial Data Governance & Mappings
SAP (ERP) · Anaplan · Oracle EPM (FCCS, PBCS)
Programming, Data & Engineering
SQL — CTEs, Window Functions, Optimization
Python — Pandas, NumPy, scikit-learn, spaCy, NLTK
ETL Pipelines · Data Modeling · Database Design
PostgreSQL · SQLite · SQLAlchemy
NLP — VADER, LDA · Machine Learning
Fuzzy Matching (RapidFuzz) · Entity Resolution
Multi-Agent LLM Orchestration (Claude API)
Docker · GitHub
AWS (Cloud Practitioner)
BI, Reporting & Business Analysis
Power BI — KPI Frameworks & DAX
Tableau · Streamlit · Plotly
Excel — Advanced, Power Query, INDEX-MATCH, Pivots
Dashboard Development & KPI Design
Requirements Gathering · UAT · JIRA
Data Validation · Data Mining · Ad Hoc Analysis
Stakeholder Management & Executive Communication
Process Improvement · Training & Enablement
Cross-Functional Collaboration
§ 5.0

Education & Certifications

Oregon State University
M.S. · Business — Financial Analytics

Quantitative coursework spanning corporate finance, predictive modeling, and applied analytics in Python & R. STEM-designated, with a focus on data-driven financial decision-making.

STEM-DESIGNATED · OPT ELIGIBLE
Sep 2024 → Jun 2026 · GraduatedGPA 3.62 / 4.0
Visvesvaraya Technological University
B.E. · Electronics & Communication Engineering

CMR Institute of Technology, Bengaluru — engineering foundation in signals, systems, and computation. The quantitative depth underlying all the analytics work.

4-YEAR ENGINEERING DEGREE
Aug 2017 → Jul 2022Bengaluru, India
AWS Certified Cloud Practitioner
Amazon Web Services
Issued Aug 2026
§ 6.0 — Contact

Let's talk
analytics.

● Open to Work · Available Immediately

I'm looking for full-time roles in Financial Analysis, FP&A, Business Analytics, or Data Analytics, anywhere in the US or remote. Graduated June 2026, work-authorized on F-1 OPT with STEM extension eligibility, and ready to start now. If you're building a team that works at the intersection of finance and data, reach out directly.

Say hello → vachanthambi1999@gmail.com ↓ Résumé (PDF) LinkedIn ↗ GitHub ↗
(541) 286-2789 Corvallis, OR · F-1 OPT · Open to relocation in/vachanthambi github.com/vachanthambi

Note on figures: metrics from Colt Technology Services (2022–2024), Oregon State University (2024–2026) and Campus Hyre (2021) come from professional work. Project figures are outputs of the code itself; where a project runs on generated or modeled data, the bullet says so. The structured report format is a design choice. This page was hand-coded. No frameworks were harmed.

© 2026 Vachan Thambi Naveen · Corvallis, OR · Available immediately