Data & AI Scientist

Key Skills

Aartificial intelligence, AWS, Azure, Data science techniques, Large Language Models - LLMs, Machine learning techniques, MLOps tools, PyTorch, TensorFlow

Job Description

About Service Line:

Our Insights and Data team helps our clients make better business decisions by transforming an ocean of data into streams of insight. Our clients are among Australia's top performing companies and they choose to partner with Capgemini for a very good reason - our exceptional people.

Role & Responsibilities:

• Engaging with SMEs to understand their requirements, gather data, develop and perform hypotheses testing for PoC and MVPs.
• Ability to scale up AI and have discussion to help clients to understand AI ethics
• Perform data explorations, analysing data distributions, correlation analysis, hypothesis testing and reporting the results in clear form to clients.
• Develop predictive algorithms such as timeseries forecasting, anomaly detection, failure prediction and report results in clear format to clients.
• Deploy the models mainly to Azure ML or AWS platform but also adopt the deployment strategy based on the stakeholders requirements.
• Maintain and update the models as needed.
• Experiences with developing LLM based RAG solutions for GenAI solutions

Qualifications and Experience:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• Proven experience in machine learning, timeseries analysis, supervised and unsupervised approaches and deep learning frameworks (e.g., TensorFlow, PyTorch).
• Familiarity with MLOps practices.
• Proficiency in Python.
• Strong communication and collaboration skills.
• Experience in deploying AI/ML solutions in Azure environments
• Experience with working with historians such as OSI PI
• Minimum 7 years experience of working with AI and Data Science models

Project Overview:

• Familiarities with Front-end developing tools.
• Familiarities with Azure Data Explorer and Azure Data Fabric. Also AWS environment.
• Experiences with optimisation algorithms. Familiarities with gradient based and gradient free optimisations algorithms are highly valued
• Experiences with developing LLM based RAG solutions