Senior Data Scientist

Building on our past. Ready for the future

Worley is a global professional services company of energy, chemicals and resources experts. We partner with customers to deliver projects and create value over the life of their assets. We re bridging two worlds, moving towards more sustainable energy sources, while helping to provide the energy, chemicals and resources needed now.

Worley Digital

At Worley, our Digital team collaborates closely with the business to deliver efficient, technology-enabled sustainable solutions, that will be transformational for Worley. This team, aptly named Worley Digital, is currently seeking talented individuals who would be working on a wide range of latest technologies, including solutions based on Automation, Generative AI.

What drives us at Worley DigitalIt s our shared passion for pushing the boundaries of technological innovation, embracing best practices, and propelling Worley to the forefront of industry advancements. If you re naturally curious, open-minded, and a self-motivated learner - one who s ready to invest time and effort to stay future-ready - then Worley could be your ideal workplace.

Major Accountabilities of Position: AI/ML Architect must have

Defining, designing, and delivering ML architecture patterns operable in native and hybrid cloud architectures.

Collaborate with Enterprise Architecture, Info Security, DevOps and Data Intelligence team to implement ML Solutions.

Defining data augmentation pipelines for unstructured data like Documents, Engineering drawings etc.

Build new network architecture in CNN/LSTM/RCNN or Develop wrapper for pre-trained models. Conduct feasibility of transfer learning fitment for given problem.

Research, analyze, recommend, and select technical approaches to address challenging development and data integration problems related to ML Model training and deployment in Enterprise Applications. Perform research activities to identify emerging technologies (Generative AI) and trends that may affect the Data Science/ ML life-cycle management in enterprise application portfolio.

Design and deploy AI/ML models in real-world environments and integrating AI/ML using Cloud native or hybrid technologies into large-scale enterprise applications .Demonstrated experience developing best practices and recommendations around tools/technologies for ML life-cycle capabilities such as Data collection, Data preparation, Feature Engineering, Model Management, ML Ops, Model Deployment approaches and Model monitoring and tuning.

Knowledge / Experience / Competencies Required

IT Skills Experience (Priority wise):

1. Hands-on programming and architecture capabilities in Python.

2. Demonstrated technical expertise around architecting solutions around AI, ML, deep learning and Generative AI related technologies.

3. Experience in implementing and deploying Machine Learning solutions (using various models, such as GPT-4, Lama2, Mistral ai, text embedding ada, Linear/Logistic Regression, Support Vector Machines, (Deep) Neural Networks, Topic Modeling, Game Theory etc. )

4. Understanding of Nvidia Enterprise NEMO Suite.

5. Expertise in popular deep learning frameworks, such as TensorFlow, PyTorch, and Keras, for building, training, and deploying neural network models.

6. Experience in AI solution development with external SaaS products like Azure OCR

7. Experience in the AI/ML components like Azure ML studio, Jupyter Hub, TensorFlow Sci-Kit Learn

8. Hands-on knowledge of API frameworks.

9. Familiarity with the transformer architecture and its applications in natural language processing (NLP), such as machine translation, text summarization, and question-answering systems.

10. Expertise in designing and implementing CNNs for computer vision tasks, such as image classification, object detection, and semantic segmentation.

11. Hands on experience in RDBMS, NoSQL, big data stores like: Elastic, Cassandra.

12. Experience with open source software

13. Experience using the cognitive APIs machine learning studios on cloud.

14. Hands-on knowledge of image processing with deep learning ( CNN,RNN,LSTM,GAN)

15. Familiarity with GPU computing and tools like CUDA and cu DNN to accelerate deep learning computations and reduce training times.

16. Understanding of complete AI/ML project life cycle

17. Understanding of data structures, data modelling and software architecture

18. Good understanding of containerization and experience working with Docker, AKS.

People Skills:

Clear and concise communication is vital for explaining complex machine learning concepts to non-technical stakeholders, presenting results, and collaborating with cross-functional teams.

Ability to work independently and as part of a team.

Being open to new ideas, embracing change, and adapting to evolving technologies and methodologies are crucial for staying relevant and effective in the rapidly changing field of machine learning.

Cooperative mindset, flexibility, and the ability to work effectively in a team.

Professional and open communication to all internal and external interfaces.

Balancing multiple projects, prioritizing tasks, and meeting deadlines while maintaining a high standard of work requires effective time management and organizational skills.

Accurately report to management in a timely and effective manner.

Other Skills:

Outstanding analytical and problem-solving skills

Education Qualifications, Accreditation, Training:

Master s in Information Technology / Big Data/Data Science/