DATA SCIENTIST

On-siteposted 1mo ago

Skills required

AI Data science Machine learning Project management Neural networks Computer vision Time series Python R SQL Spark AWS Redshift

About this role

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

Responsibilities

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.