Luciano P.
Data Scientist
He specializes in developing predictive and analytical solutions for churn prediction, recommendation systems, anomaly detection, and NLP-based insight extraction, with additional experience in LLM and RAG systems.
His expertise covers the full data science lifecycle, from ETL and data preparation to model development, evaluation, and advanced data visualization. Luciano also works with AWS and Azure to build scalable and efficient data solutions that support production use cases.
With a strong focus on turning complex datasets into actionable insights, Luciano applies data science and AI techniques to support informed decision-making, improve business processes, and drive measurable business outcomes.
Main expertise
Data Science9 years
Python9 years
SQL7 years
Pandas6 yearsExperience4
Head of Data
- Managed and mentored a multidisciplinary team of data scientists, engineers, and analysts, fostering a collaborative environment focused on innovation, continuous learning, and high-impact delivery.
- Provided technical guidance and strategic direction across projects, ensuring alignment with business objectives and successful execution of data initiatives.
- Designed and implemented data-driven solutions to complex business problems, translating stakeholder requirements into scalable models and actionable insights.
- Championed best practices in data science, analytics, and engineering, improving workflow efficiency and overall output quality.
MySQL
Apache SparkPython
Microsoft Power BI
NumPy
Senior Data Scientist
- Served as a Senior Data Scientist, leading the development and deployment of a debt collection model.
- Analyzed historical payment behavior and customer data to identify key predictors of debt recovery.
- Deployed the model to production and monitored its performance over time.
- Provided actionable insights to the collections team, improving targeting strategies.
- Contributed to measurable improvements in recovery rates and reductions in operational costs.
Apache SparkPythonData SciencePandas
Scikit-learn
Data Scientist
- Automated content tagging using NLP techniques such as Word2Vec, FastText, SVM, and Naive Bayes with Python and AWS.
- Developed audience prediction models using Random Forest and linear regression with R and Python.
- Built recommendation systems using association rules and collaborative filtering with R.
- Created dashboards by preparing data with SQL, developing models with R, and visualizing results using R and Periscope.
PostgreSQL
AWSPythonNumPyPandas
Data Scientist
- Credit risk modeling using Gradient Boosting for Banc Sabadell
- (SQL, R).
- Text Analyzing of keywords using NLP in R (text cleaning and
- Transformation, text classification usingSVM).
- Analytical tools as a R package (R, Spark, Hive, H20).
- Audience prediction for Mediaset (Spanish TV) using ARIMA (R).
- Air quality prediction usingGradient Boosting (Python, R).
AWSApache SparkPythonNumPyPandasEngineering excellence
All Data Scientists who have applied to Proxify are scored from 0 to 300 on engineering excellence, one of the five parameters we evaluate. This score reflects engineering excellence only, based on interviews, take-home assignments, live coding sessions, and/or on-the-job performance reviews. The curve shows how all evaluated Data Scientists are distributed across that range, where our acceptance threshold for this parameter sits, and where Luciano stands.
Score 210 · Top 5% of engineers
Education
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