Fernando G.

Clearco is a revenue-based financing platform that provides non-dilutive capital to thousands of e-commerce and SaaS businesses, using data-driven underwriting models.

• Built an LLM-based document extraction pipeline (Ollama, Phi-4, Docling, Vertex AI), reducing client document processing from days of manual review to minutes of automated extraction

• Migrated revenue forecasting for thousands of businesses from daily to weekly cadence, cutting compute time by 14% while preserving forecasting accuracy

• Implemented an uncertainty quantification framework using conformal prediction, replacing point-in-time forecasts with calibrated uncertainty bands to flag worst-performing forecasts

• Drove the analytical decision on launching a new banking-data-based financing product, providing the modelling that compared it against the existing revenue-based offering

• Diagnosed and eliminated a recurring timeout issue in the production forecasting system, removing a long-standing source of failed runs

• Designed an end-to-end automated ETL pipeline (Snowflake, Airflow, Cloud Composer) to generate datasets for forecasting research at scale

• Built a Streamlit-based annotation and metrics suite to evaluate LLM extraction quality across iterations

Zixen Trading is a high-frequency crypto trading venture co-founded with former Transfero colleagues to develop systematic trading strategies on digital assets.

• Co-founded the company and shaped its initial technical direction in systematic crypto trading

• Built the PnL tracking infrastructure that connected to multiple crypto exchange accounts and ingested live market data

• Developed the data layer used by trading strategies for backtesting and live execution

• Owned the full ML and data engineering stack as a founding technical lead during the build phase

• Designet og utviklet datapipeliner for å beregne massive økonomiske datasett, noe som reduserte beregningstiden fra flere dager til kun timer (PySpark – Databricks – Azure);
• Laget en «metode på kode» for å veilede utviklingen av nye maskinlæringsprosjekter, der to prosjekter ble strukturert;
• Utviklet maskinlæringsmodeller for å forutsi Bitcoin-handelsvolum (XGBoost, 65 % nøyaktighet) og kryptovolatilitet (Temporal Fusion Transformer, 95 % nøyaktighet);
• Utviklet en MLOps-pipeline for å sette modeller i produksjon: beregning av hundrevis av funksjoner, strømmeslutninger i sanntid ved bruk av FastAPI Websocket-server, modellovervåking ved hjelp av SQL-database og Slack API (Docker på ECS, AWS FeatureStore, FastAPI, SQL, Slack), med en oppetid på over 99 %;
• Laget en numerisk indeks for å klassifisere tidsseriedata og bidra til å bygge handelsstrategier (statistiske korrelasjoner, ko-integrasjon, ACE-score, tidsserieanalyse).

Utførte statistisk analyse av kundefrafall for en stor brasiliansk B2C videostrømmingsapp.

• Utviklet algoritmer for GPS-filtrering, korreksjon og ytelsesanalyse for amatørfotballspillere, samt elitefotballspillere.
• Tok ansvaret for å kvalitetssikre dataprodukter og administrere oppgaver som backlog, OKRs og metrikk.
• Integrerte Python-algoritmer med AWS på Serverless Framework, inkludert Lambda, S3, EC2, DynamoDB og CloudWatch.
• Utførte oppgaver som dataanalyse, implementering av Kalman-filtre, nevrale nettverk, fuzzy-filtre, vitenskapelige algoritmer, statistisk innsiktsmotor, grunnleggende visuelt innhold og algoritmer for ansiktsgjenkjenning.
• Brukte Python til å lage kraftige datavisualiseringer.
• Opprettholdt Python-infrastrukturen for automatisk levering av hundrevis av daglige rapporter.

• Data Scientist from Day 1 of the company, from 0 to over 50000 users

• Algorithms for GPS filtering and correction and performance analysis fore lite football athletes and amateur players

• Client-facing customer success role regarding data services of the product

-Responsible for data products quality assurance, backlog, OKRs, and metrics

• Integrated Python algorithms with AWS on Serverless Framework (Lambda, S3 , EC2, DynamoDB, CloudWatch)

-Tasks included Data Analysis , Kalman Filters , Neural Networks , Fuzzy Filters , scientific algorithms , statistical insights engine ,basic computer vision and facial recognition algorithms

• Powerful data visualization with Python

• Maintenance of Python infrastructure for automatic delivery of hundreds of reports daily