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Resume · Finance & dataAs of October 2026

Davirson Novoa Ramírez

Finance Data Analyst · Financial BI Analyst · Analytics Engineer

I find the variance, I trace it to root cause, I leave the control running itself, and I explain it where the decision gets made. All four, with no translator between the business and the data.

Bogotá, Colombia · GMT-5 · Fully remote · Daily working English with US and European teams · Native Spanish

  • 15+countries whose SG&A close and forecast I own, from HQ
  • 60 hrs/moof manual treasury work eliminated (10 per analyst)
  • 2 promotionsin 26 months at SLB: from treasury to billing specialist
  • 95 Mcredit records processed on a laptop

Profile

Finance Data Analyst who owns the SG&A close and forecast for 15+ countries, from HQ. An economist trained in treasury, SOX billing and FP&A, who builds the data the business runs on instead of waiting for it: Python, SQL and Power BI on real operations. Alongside it I run a PostgreSQL warehouse with dbt, 89 automated quality tests and a Power BI semantic model versioned as code, plus a credit decisioning system over 64 million+ records with ten governance gates. Every technical figure on my site links to the repository that produces it.

The crossover role

Hire me for what I already do in every finance role: build the data the business needed, which nobody else was going to build. The edge is not knowing Python — plenty of people do. It is knowing which question is worth answering before writing the first line.

Experience

Neoris EPAM

Bogotá, Colombia · remote

Business Consultant, FP&A

Mar 2026 — present
  • Own the SG&A close and forecast process for 15+ countries across the Americas and the Eastern Hemisphere (India, Hungary, Spain, Czech Republic), from HQ.
  • Accountable for the monthly close and company-wide forecast confirmation; I explain every variance against plan and prior forecast.
  • Isolate the FX effect in constant currency and per-currency inflation, so the operating variance reads clean.
  • Built the FP&A reporting on advanced Excel, Power Query, Power BI, MicroStrategy, JD Edwards and SAP.

SLB

Bogotá, Colombia · hybrid

Two promotions in 26 months: from treasury to billing specialist for Argentina and Brazil, with an intern reporting to me.

Billing Specialist (Argentina & Brazil)

Dec 2024 — Mar 2026
  • Owned revenue recognition in SAP for Argentina and Brazil under Sarbanes-Oxley (SOX) controls.
  • Supervised and trained an intern on the billing team.
  • Built Power BI models and visualizations for revenue and trend analysis.
  • Audited integrated technology projects, with end-to-end traceability and compliance.

Treasury Analyst

Oct 2024 — Dec 2024
  • Gave the team back about 60 hours a month — roughly 10 per analyst — by automating manual treasury reporting.
  • Automated bank reconciliation in SAP and moved it into Power Automate workflows.

Treasury Intern

Jan 2024 — Jun 2024
  • Detected and quantified FX mismatches across Latin American markets in Python, identifying financial risks and hedging opportunities.
  • Created Power BI dashboards for cash-flow projections across 15+ countries.
  • Automated bank transaction reporting across 10+ regional operations.

LEE Javeriana

remote

Research Assistant

Jul 2023 — Dec 2023
  • Modelled Colombian higher-education data for impact-evaluation studies.
  • Published the research findings in Power BI dashboards.

Projects in operation

Open source and checkable; for Kairo, the proof is the public demo. None of this is a course exercise.

credit-risk-mlops

2026 — deployed, scoring live

$276.3M in avoidable charge-offs · AUC 0.7005 · 10 gates, and one blocks my own model

Credit decisioning system with model governance — design and build

github.com/DavinsonR/credit-risk-mlops
  • Declining the riskiest 10% of the test portfolio would have avoided $276.3M in charge-offs — 2.15x what random declines achieve — and the report ships the counterweight too: $1.99B in good lending forgone.
  • Deployed loss model on 1.96M SBA 7(a) loans: AUC 0.7005 out of time, +0.0311 over an interpretable WoE scorecard, with calibration error 0.0107. The second model, for credit access over 62.4M HMDA applications, is the one the fairness gate refuses to promote; the event study runs separately, over 93.4M applications.
  • The first reading was 0.9461, and it was not a model but a leak: TermInMonths is overwritten when a loan is liquidated, so the field carried the outcome. Removing it drops the ablation to 0.6621 — and that is the number published.
  • Ten promotion gates judged by exit code, one of them designed to block my own model when it misses the threshold.
  • Monitoring caught the source changing its vocabulary without notice: a field stopped meaning the same thing between vintages, and nobody would have known without the control.
  • The production ONNX artifact scores in the reader's own browser — no server, and no data leaves the page.
PythonLightGBMscikit-learnoptbinning (WoE)MLflowONNXDuckDBpanderaDockerPower BI

market-data-medallion

2026 — in daily operation

1,392 strategy variants validated out of sample · 48 assets a day · 89 quality tests before anything ships

Market data platform — designed, built and operated

github.com/DavinsonR/market-data-medallion
  • Watermark-based incremental ingestion from 4 sources (Tiingo, Coinbase, Kraken, FX) into a three-layer PostgreSQL warehouse: 48 assets and more than 58,000 candles, idempotent and resumable.
  • 89 automated dbt data-quality tests plus 171 Python unit tests; the pipeline fails before publishing bad data, not after.
  • No-look-ahead backtesting engine with 70/30 out-of-sample validation over 1,392 strategy variants: the vast majority of in-sample winners did not survive the blind window.
  • FX decomposition for Latin American ADRs, separating company performance from the currency move through the identity (1+r_USD) × (1+r_FX) = (1+r_local).
  • Daily orchestration on GitHub Actions with a rate-limit circuit breaker, running on zero-cost infrastructure.
PostgreSQLMedallion architecturedbtPythonPrefectGitHub ActionsSupabasePower BI (TMDL / PBIP)Next.js

Kairo — multi-user product with sensitive data

2026 — in production · open demo

2,741 tests across 8 CI gates · 65 tables and 46 views under row-level security · a public demo that never touches the database

Own product — designed, built and operated

public demo, no account
  • Multi-user model on Postgres with row-level security on all 65 tables: the conversion required rebuilding 22 primary keys that identified rows without the user, and pushing the user inside the foreign keys so isolation did not rest on the policy alone.
  • Eight CI gates judged by exit code: types, format, lint, 2,741 tests, build, dependency audit, a guard that hunts personal data inside the built artifact, and a smoke test requiring the anonymous role to be denied across all 111 tables and views.
  • A public demo running the five real screens over 120 seed-generated days that never queries the database; a test walks the runtime import graph and fails if any reachable module gets to the data client.
  • Day logging in one form and one button, with eight independent writes, an on-device draft and send queue to survive losing signal, and a purpose-built number reader because a browser number field was storing expenses divided by a thousand.
Next.js 16React 19TypeScriptPostgres (Supabase)RLSTailwindPWAVitest

Research

My own research, built in public on open data.

Financial inclusion and regional growth in Colombia

2026 — thesis filed

19 public sources · 1,123 municipalities · 160 specifications, each with its power

M.Sc. in Economics thesis, Javeriana — reproducible research end to end

repository on GitHub
  • Purpose-built open-data downloader with a verifiable manifest: nineteen sources (financial supervisor, statistics office, ICT and education ministries, national map), row counts verified against the source, metadata-driven typing and a sha256 per file.
  • Dimensional warehouse in dbt on DuckDB: department, municipality and period dimensions, financial inclusion in long form by product block, and tests that reconcile each department total with the sum of its municipalities.
  • Geographic keys resolved to the municipal code with 100% coverage across the 34 quarterly cuts, and the splice between the supervisor's two tables measured quarter by quarter.
  • A financial-inclusion index by dimension and annual panels at department and municipality level, with an explicit battery against spurious correlation. Public logbook of mistakes and wins.
dbtDuckDBPythonParquetPanel dataQuarto

Skills

Finance domain

What turns a data point into a business decision.

FP&AClose & forecastSG&ARevenue recognition (SOX)TreasuryFX analysisBudgetingConstant currencySAPJD EdwardsMicroStrategyVariance analysisMonth-end closeCash-flow forecastingManagement reportingAccount reconciliation

Data & engineering

Tools running today in a public repository, not on a certificate.

Analytical SQLPostgreSQLDuckDBdbtMedallion architecturePython (pandas)Incremental ETLPrefectGitHub ActionsSupabasePower BI · DAXPower QueryTableauGitNext.js · VercelPanel econometricsFixed effectsCausal inferenceTime seriesCredit scorecards (WoE)LightGBMMLflowONNXMLOpsData testingData warehousingDimensional modeling (star schema)ELTCI/CDDashboarding

Technical stack

Every tool links to the exact work where I used it.

Financial analysis and FP&A
SG&A close and forecast across 15+ countries: variance against plan and in constant currency
SAP
Revenue recognition under SOX and automated bank reconciliation, at SLB
Excel and financial modeling
The close and forecast for those 15+ countries, at Neoris EPAM
Python
Incremental ingestion, backtesting engine, FX decomposition
SQL · PostgreSQL
Three-layer medallion warehouse, more than 58,000 candles in production
dbt
89 quality tests that run before a single figure is published
DuckDB
Dimensional warehouse over 19 public sources resolved to municipal codes
Git · GitHub Actions
Daily cron in operation, with a rate-limit circuit breaker
Panel econometrics
Two-way fixed effects over 228 observations, wild cluster bootstrap and permutation placebo
Diagnostics and robustness
Measured cross-sectional dependence, CCE, SLX, pre-trends, cluster-robust wild bootstrap and a Holm correction
Machine learning
LightGBM over 1.96M SBA loans: AUC 0.7005 out of time, +0.0311 over the interpretable scorecard
Credit risk and scorecards
WoE binning with optbinning, out-of-time validation across the COVID shock, calibration error 0.0107
Model governance (MLOps)
Ten promotion gates judged by exit code; one blocks my own model

Education & certifications

M.Sc. in Economics

THESIS FILED

Pontificia Universidad Javeriana

2025 — 2026

Thesis filed in August 2026; graduation expected in November 2026. It is the origin of the financial-inclusion project I keep building in public.

see the research →

B.Sc. in Economics

COMPLETED

Pontificia Universidad Javeriana

2020 — 2024

Recognition

Winner — BodyTech Trends Hackathon

2024

Search-demand analytics for a gym chain: 19,560 keyword records cleaned in Python and a branch-by-branch trend dashboard in Tableau. Still published and open to anyone.

Tableau dashboard with the estimated average revenue per customer from 2022 to 2025, search occurrence by month and six keyword trends.
see the dashboard on Tableau Public ↗

Ecopetrol Scholar — Mario Galán Gómez Program

2018

Scholarship for academic merit and leadership potential.

Machine Learning Specialization

2024

Stanford · Coursera

Cybersecurity Certificate

2024

Google · Coursera

Remote-ready

My entire track record is remote or hybrid, with operations across 15+ countries.

GMT-5 (Bogotá): your US and Canadian hours, in full.

Daily working English with teams in the US and Europe (Hungary, Czech Republic, Spain).

Native Spanish · English B2 · Portuguese A2.

Stop choosing between the person who understands the business and the person who builds the data.

One hire covers the financial analysis, the pipeline and the dashboard. Finance Data Analyst, Analytics Engineer or FP&A automation. Bogotá, GMT-5, your US hours in full. I reply within one business day, in English or Spanish.

A 20-minute video call: you pick the slot in your time zone and the link arrives by email.

davirson@davirson.com

Full version (3 pages) · LaTeX source · Compiles in Overleaf with nothing to install.