Principal Data Scientist

Role Summary

As a Data Science Lead at Worley, you will collaborate with an established team to deliver high-impact data science and AI/ML projects for clients, while continuing to expand your own technical expertise. The role focuses particularly on working with unstructured data and involves designing scalable AI/ML solutions, staying on the leading edge of technology, and driving innovation across both internal and external stakeholders.

Key Responsibilities

• Platform & Tool Development:
• Conceptualize, build, and manage AI/ML platforms with a focus on unstructured data.
• Evaluate and select the best AI/ML tools and frameworks in the industry.
• Leverage existing frameworks, standards, and patterns to create scalable architectural foundations.
• Solution Delivery & Ownership:
• Lead the development of cognitive solutions for both internal stakeholders and external customers.
• Conduct research in areas like Explainable AI, image segmentation, 3D object detection, and statistical methods.
• Ensure delivery of scalable, enterprise-grade applications and services.
• Strategic Insight & Innovation:
• Analyse global market trends (economic, social, cultural, technological) to identify AI/ML opportunities.
• Shape value propositions and recommendations using a global perspective.
• Evaluate algorithms, models, and emerging technologies to optimize organizational investment.
• Collaboration & Leadership:
• Guide and support data scientists and engineers in project execution involving large and complex datasets.
• Establish and oversee AI/ML lifecycle processes and best practices.
• Ensure collaboration across multi-disciplinary teams.

Technical Skills & Experience

• AI/ML Expertise:
• Deep understanding of the full AI/ML project life cycle.
• Expert in deep learning and reinforcement learning.
• Experience with MLOps practices for effective model lifecycle management.
• Proficient in machine learning algorithms: k-NN, GBM, Naive Bayes, SVM, Neural Networks, Decision Forests.
• Tools & Frameworks:
• Hands-on experience with tools such as:
• Jupyter Hub, Zeppelin Notebook, Azure ML Studio
• TensorFlow, TensorFlow-Keras, PyTorch, SciKit-Learn, Spark MLlib
• Strong knowledge of CNN, R-CNN, LSTM, Encoder/Transformer architectures.
• Experience with deep learning models like Inception-ResNet and ResNeXt-50.
• Proven use of RNNs for text, speech, and generative models.
• Data Engineering & Storage:
• Familiarity with NoSQL databases (GraphX, Neo4J), document, columnar, and in-memory data models.
• Experience with ETL tools and platforms such as Talend, SAP BI Platform, SSIS, and MapReduce.
• Visualization & Reporting:
• Skilled in building KPI dashboards and storytelling visuals using tools like Tableau or Zoomdata.

Preferred Qualifications

• Advanced degree in Data Science, Computer Science, AI/ML, or related fields.
• Demonstrated leadership in managing AI/ML projects and teams.
• Prior experience in consulting or energy/resources industries is a plus.