Samuel P.
Data Scientist
Among his key achievements, Samuel designed and implemented an automated data workflow using Snowflake, Python, and Power BI, which reduced manual processing by 90%, improved data quality by 20%, and supported over 10 successful product launches within two years. This innovation significantly enhanced company-wide data accessibility and decision-making.
Recognized for his ability to translate complex data into clear, impactful insights, Samuel fosters strong cross-functional collaboration and consistently drives data-driven transformation across business functions.
Main expertise
R (programming language)6 years
SQL8 yearsExperience4
Data Scientist
- Optimized a production-grade behavioral classification model across 2M+ entities, identifying and flagging high-risk user anomalies that drove measurable financial impact.
- Developed auditable ETL/ELT data pipelines using Snowflake, Python, and dbt, achieving a 92% reduction in manual data handling while ensuring data quality and regulatory compliance.
- Collaborated with Product, Sales, and Marketing teams to design and deploy dashboards that monitored and evaluated product launch performance.
Qlik Sense
PythonSQL
Oracle
Microsoft Power BI
Data Engineer
- Designed and developed end-to-end data pipelines in Azure and Databricks, integrating cross-functional datasets at scale and reducing analysis time by 40%.
- Designed and optimized data models for enterprise-level reporting, ensuring robust data governance and standardized KPIs across multiple geographies.
- Improved cross-country reporting consistency, reducing data discrepancies by 25% and strengthening reporting reliability.
MongoDB
DatabricksPythonSQL
Azure
Data Scientist
- Significantly improved data validation protocols, ensuring dataset integrity for critical decision support systems in alignment with Ethical and Responsible AI standards.
- Trained and validated predictive AI/ML models on complex biomedical datasets, improving diagnostic accuracy by 5%.
- Adapted analytical workflows to be scalable and cloud-ready within Python and R frameworks, reducing computing time by 53%.
MongoDB
AWSPythonSQL
BashPhD Statistician
- Developed statistical and machine learning frameworks for high-dimensional genomic and environmental data, resulting in five peer-reviewed publications.
- Optimized regression, Bayesian, and machine learning models for biological datasets, improving predictive accuracy by 12%.
PythonSQLBash
NumPyAssessments
Engineering excellence
Samuel’s overall performance in a 90-minute live technical assessment ranks in the top 15% of vetted Data Scientists at Proxify.
Certificates 3
Issued Mar 2025
PythonSQL
SnowflakeIssued Mar 2023
TableauIssued Mar 2025
PythonSQLSnowflakePortfolio
Highlighted by Samuel
2A repository for exploring and analyzing data related to Belgian breweries and their beers. Ideal for practicing go-to-market strategies.
Other projects 2
Education
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