Transforming Engineering through Data Science

Agus Jamaludin, Data Scientist Lead, Petrosea

Agus Jamaludin is a seasoned Data Scientist with over nine years of experience, specializing in data architecture, pipeline development, predictive analytics (ML/AI) and data visualization. As the Data Scientist Lead at Petrosea, he drives datadriven decision-making by designing scalable data solutions, optimizing operations with predictive models and developing interactive dashboards using Power BI and SAP Analytics Cloud. Agus collaborates closely with IT, operations and business units to align data strategies with business objectives.

Through this article, Jamaludin highlights the need for a blend of technical, business and leadership skills to drive innovation and efficiency in engineering data science.

In the engineering sector, a data scientist must combine technical and business skills to drive operational efficiency, predictive maintenance and data-driven decision-making. Data engineering and pipeline development are essential for handling large-scale sensor, IoT and operational data while selecting the correct big data or traditional technologies for efficient processing. Advanced analytics and modeling are critical in defining the right analytical approach based on business goals and data characteristics, whether structured, unstructured, time-series, or geospatial. Data visualization and BI tools are crucial for effectively communicating insights through dashboards and reports, ensuring that analytics drive informed decision-making. Equally important is business and domain knowledge, particularly in mining and engineering operations, to apply analytics in key areas such as equipment reliability, safety, production planning and resource optimization. A strong understanding of cost optimization, risk management, stakeholder communication and compliance ensures that AI-driven solutions align with business objectives while adhering to industry standards.

From a technical perspective, data science challenges in engineering services are similar to those in other industries. These include handling noisy data that requires extensive cleaning and managing massive datasets efficiently to optimize resource allocation and costs. From a business perspective, engineering services present unique challenges. Heavy machinery failures can cause significant downtime and financial losses, making traditional preventive maintenance inefficient. By integrating engineers' expertise with analytical approaches like anomaly detection and machine learning, we developed predictive models to estimate the Remaining Useful Life (RUL), optimizing maintenance schedules and reducing costs.

Resource allocation is another critical challenge in engineering projects. Poor equipment, labor and materials optimization leads to cost overruns and delays. We addressed this by developing dynamic optimization models for resource allocation, such as trucks and excavators.

Additionally, many engineering professionals are unfamiliar with AI/ML, leading to resistance to adopting data-driven insights. To build trust, we collaborated closely with engineers, integrated models into user-friendly dashboards and conducted training sessions to facilitate adoption.

At my company, we embrace the ACTION core values shaping my leadership approach. "A" for Agile means fostering flexibility, continuous learning and quick adaptation to industry changes. "I" for Innovative encourages creativity, experimentation and leveraging advanced data science techniques for impactful solutions. "O" for Open-Minded promotes knowledge sharing, cross-functional collaboration and openness to diverse perspectives. By embedding these principles, I ensure my team remains innovative, adaptable and resilient in a fast-evolving industry.