Ryan Heng Boon Choon
Data Scientist Intern – Sanofi Manufacturing, Singapore
May 2025 – Apr 2026, (1 Year)
Built and maintained ETL data pipelines integrating MES, DCS, and historian data, enabling reliable access to high-frequency manufacturing
datasets for analytics and modeling
Performed exploratory data analysis on process and equipment data to identify trends, anomalies, and key drivers impacting production
performance
Developed and evaluated time-series models (ARIMA, LSTM) to forecast process variables and support early detection of equipment or process
deviations
Implemented anomaly detection techniques to identify abnormal patterns in manufacturing data, improving monitoring of process stability and
quality metrics
Collaborated with cross-functional teams (Engineering, Automation, Quality) to translate data insights into actionable improvements for yield,
throughput, and downtime reduction
Developed data transformations and SQL queries in Snowflake and MSSQL to support scalable data storage and analytics workflows
Built dashboards and visualizations to communicate key metrics and insights to technical and non-technical stakeholders
Data Engineer Intern – Power Hydraulics, Singapore
Mar 2023 – Aug 2023 (6 Mths)
Calibrated and optimized PLC, SCADA, and DCS control logic systems for marine vessel ballast water systems ensuring structural integrity, and
improve maneuverability
Built and improved batch streaming data pipelines for sensor and equipment telemetry
Automated test data analysis using Python and MATLAB scripts, reducing manual analysis effort and increasing test throughput for engineering
validation workflows
Associate Researcher Intern – Procter & Gamble, Singapore (SkinCare department)
Aug 2019 – Mar 2020 (6 Mths)
Built and analyzed experimental datasets from skincare formulation trials using Python and Excel, applying statistical methods to identify key
drivers of product stability and performance
Developed data-driven insights from laboratory and consumer testing data, enabling optimization of formulations based on efficacy, texture, and
user experience metrics
Automated data cleaning and preprocessing workflows for experimental results, improving analysis efficiency and reducing manual errors in
reporting
-
Data Scientist Intern at Sanofi Manufacturing
May 2025 - Apr 2026, -
Built and maintained ETL data pipelines integrating MES, DCS, and historian data, enabling reliable access to high-frequency manufacturing
datasets for analytics and modeling
Performed exploratory data analysis on process and equipment data to identify trends, anomalies, and key drivers impacting production
performance
Developed and evaluated time-series models (ARIMA, LSTM) to forecast process variables and support early detection of equipment or process
deviations
Implemented anomaly detection techniques to identify abnormal patterns in manufacturing data, improving monitoring of process stability and
quality metrics
Collaborated with cross-functional teams (Engineering, Automation, Quality) to translate data insights into actionable improvements for yield,
throughput, and downtime reduction
Developed data transformations and SQL queries in Snowflake and MSSQL to support scalable data storage and analytics workflows
Built dashboards and visualizations to communicate key metrics and insights to technical and non-technical stakeholders -
Data Engineer Intern at Power Hydraulics
Mar 2023 - Aug 2023 -
Calibrated and optimized PLC, SCADA, and DCS control logic systems for marine vessel ballast water systems ensuring structural integrity, and
improve maneuverability
Built and improved batch streaming data pipelines for sensor and equipment telemetry
Automated test data analysis using Python and MATLAB scripts, reducing manual analysis effort and increasing test throughput for engineering
validation workflows -
Associate Researcher Intern at Procter & Gamble
Aug 2019 - Mar 2020 -
Built and analyzed experimental datasets from skincare formulation trials using Python and Excel, applying statistical methods to identify key
drivers of product stability and performance
Developed data-driven insights from laboratory and consumer testing data, enabling optimization of formulations based on efficacy, texture, and
user experience metrics
Automated data cleaning and preprocessing workflows for experimental results, improving analysis efficiency and reducing manual errors in
reporting
-
Bachelor's degree in chemical engineering at Technical University of Munich
Aug2022/Apr2026
- Data analysis
- deep technical understanding of oil and gas commodities
- optimisation
- Statistics
Updated 2 months ago