Job
Description
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.
