Mariana F.
Data Scientist
Hun har også praktisk erfaring med unit- og integrationstest, feature store management og værktøjer som Databricks, Looker og Metabase. Hendes velafrundede tekniske færdigheder giver hende mulighed for at opbygge pålidelige og skalerbare dataløsninger.
Mariana har en stærk baggrund inden for økonomi og marketing og er gået over til teknologibranchen, hvor hun har fire års erfaring. Hendes ekspertise spænder over analyse, kreditmodellering og anbefalingssystemer, drevet af hendes passion for data og statistik. Hun udmærker sig ved at skabe veludformede, datadrevne produkter, der giver meningsfuld indsigt og resultater.
Med en unik kombination af analytisk skarpsindighed og tekniske færdigheder er Mariana dygtig til at drive datadrevne beslutninger og opbygge effektive produkter, hvilket gør hende til et aktiv for ethvert datacentreret team.
Hovedekspertise
Apache Spark5 år
AWS5 år
Databricks5 årErfaring6
Data Scientist
- Improved credit approvals from 20% to 50% by developing and deploying a gradient-boosting classification model.
- Increased calculation accuracy by 30% by automating the calculation of Net Present Value (NPV) from Excel to Python.
- Decreased manual analysis from 20% to 10% in offline tests by creating a fallback logistic regression model for credit approvals.
Python
SQLData Scientist
- Increased 10% email click-through rate by training, optimizing, and deploying a deep learning two-tower content-based recommendation model.
- Trained and optimized a deep learning collaborative filtering recommendation model for email campaigns.
- Responsible for planning 2023 bets, interviewing and hiring, onboarding three new hires, and documenting legacy decisions from previous owners of recommendation models at the company.
Data Scientist
- Improved credit approvals from 20% to 50% by developing and deploying a gradient-boosting classification model.
- Increased calculation accuracy by 30% by automating the calculation of Net Present Value (NPV) from Excel to Python.
- Decreased manual analysis from 20% to 10% in offline tests by creating a fallback logistic regression model for credit approvals.
PythonSQLData Analyst
- Improved accuracy in the estimation of supply availability duration by 20% by developing a Survival Analysis method, impacting Unit Economics estimation.
- Zeroed differences in numbers of the main business metrics across the company by developing a standardized metrics calculation in Metrics Governance initiatives.
- Freed 25% of work time from one analyst by translating the calculation of business targets from Excel to SQL.
- Provided reports of main business metrics for Series D and Series E investment rounds.
PythonSQL
Data Analyst
- Developed a report template and process for analysis of marketing strategies of competitors across the company.
- Developed marketing campaign results template standardized cross-company to automate performance analysis.
PythonSQL
Controller Assistant
- Calculated yearly and monthly forecast estimation for financial statements within 15% of deviation from actual values.
- Developed dashboard templates and trained teams across countries for reporting.
PythonSQLVurderinger
Ingeniørmæssig fremragendehed
Mariana samlede præstation i en 90-minutters teknisk vurdering i realtid er blandt de top 25% bedst kontrollerede Data Scientist hos Proxify.
Uddannelse
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