Data Scientist

Identify and define business problems suitable for AI solutions; translate ambiguous business needs into clear data science problems; develop and implement optimized AI models that drive measurable business impact, improve efficiency, and support strategic objectives

• Identify and define business problems suitable for AI/ML solutions

• Translate vague business questions into clear, solvable data science problems

• Align AI solutions with strategic business goals and ROI expectations

• Design, prototype, and develop ML models using structured and unstructured data

• Pull datasets from project management tools, ERP systems (e.g., SAP), BIM software, and IoT sensors

• Apply machine learning techniques (e.g., regression, clustering, neural networks) for predictive and prescriptive analytics

• Develop models for predictive maintenance, risk forecasting, supply chain optimization, and quality control.

• Serialize models and support deployment into production environments

• Collaborate with engineers to productionize model retraining and serving systems

• Monitor and optimize model performance; detect and address model drift

• Ensure reliability, robustness, and scalability of deployed AI systems

• Implement project-specific AI standards, protocols, and best practices

• Identify and mitigate risks associated with model predictions and usage

• Ensure quality assurance via computer vision and other techniques for defect detection and process compliance

• Apply deep understanding of EPC / Marine project lifecycle (FEED to commissioning)

• Perform time series forecasting, geospatial analysis, and simulation modeling

• Apply statistical techniques including GLM, regression, random forests, boosting, and text mining

• Create insights from large datasets using Python, R, SQL, Spark, and cloud tools (e.g., AWS S3, Redshift)

• Work cross-functionally with project managers, engineers, and site personnel

• Communicate technical findings and model insights in business terms

• Stay updated on emerging data science practices and apply them where relevant.

Bachelor’s degree in engineering (Preferably Computer Science or Information Science), or equivalent