* Automating Analytics Workflows with AI agents: Using AI Agents to migrate SQL Server databases to Redshift in an automated fashion.* Automating Analytics Ad-hoc requests with AI agents: Building a vector database of business assets and data tables to inform agents to build queries quickly and effectively. * Setting Up Data Science Workflows: Developing and implementing ways of working for data science initiatives, including the creation of data models and machine learning solutions using the Azure cloud environment.* Modeling for Personalisation & Customer Insights: Developing personalisation models to enhance customer experiences within our cashback programs, including customer journey mapping and propensity modeling.* Cross-Functional Collaboration: Partnering with digital and marketing teams to leverage data-driven insights that highlight benefits for merchants and optimize marketing strategies.* Model Deployment & Management: Overseeing the full lifecycle of data science models from development to deployment in collaboration with the Data Engineering Team, ensuring high performance and scalability.

I developed an AI-driven platform to automate and enhance businesses' online presence.Generative AI Implementation: Built Transformer models using Huggingface and Open AI models to automate creation of SEO-optimized content like blog posts and service descriptions.Retrieval-Augmented Generation (RAG): Applied RAG techniques (langchain, vector database) to improve content relevance by combining LLMs with business data. Furthermore, I implemented multi-modal retrieval mechanisms to understand data across modalities.Natural Language Processing Techniques: Analysed Instagram data using NLP to generate insights and align content with brand voice.API Development: Created RESTful APIs for seamless integration with Instagram API, Payload CMS and Vercel deployment environment.

* Developed an AI-driven solution for analyzing chat transcripts, integrating Large Language Models (OpenAI/llama), NLP, ChatGPT, and Langchain technologies, which amplified customer service productivity by an unprecedented 150%.-Spearheaded end-to-end data science initiatives, encompassing stakeholder collaboration, model development, and the establishment and maintenance of robust data pipelines using Python, PySpark, Databricks, Redshift, and Snowflake, ensuring projects aligned with strategic goals and delivered tangible value.- Engineered and deployed User Propensity models leveraging machine learning techniques, achieving a 15% uplift in user conversion rates and successfully scaling the solution to support daily engagement for over 50,000 users.- Crafted and implemented a Probabilistic Churn Model for the marketing team, enhancing customer engagement by 10% through targeted strategies based on model predictions.- Authored detailed experiment plans for A/B testing to rigorously evaluate the real-world impact and causality of deployed models, ensuring data-driven decision-making processes.- Oversaw the management and deployment of projects within an MLOps framework, utilizing GitHub Actions and Terraform to facilitate efficient, scalable solutions, demonstrating a commitment to best practices in software development and data science.- Led the rapid prototyping of technical projects, collaborating closely with international stakeholders in Japan, USA, and Australia to validate business value and strategic alignment, fostering a global perspective in solution development.- Maintained a steadfast focus on aligning data projects with stakeholder expectations and strategic objectives, leveraging Objectives and Key Results (OKRs) to guide project direction and measure success, ensuring alignment with organizational goals and maximizing ROI.*

Collaborated within a global, remote team, contributing both independently and as a key team member on data science projects.- Analysed extremely large datasets (exceeding 10 TB) utilising cloud architectures, including AWS, Redshift, and SQL, leveraging distributed computing frameworks like PySpark for scalable processing.- Designed and implemented clustering algorithms using Python, Scikit-learn, and Pandas to identify user behaviour patterns, resulting in a 15% improvement in targeted marketing strategies.- Developed a time series forecasting model to predict revenue movements accurately, enhancing financial planning accuracy by 20%.- Conducted experimental research applying NLP techniques for user game sequence similarity.- Formulated and executed rigorous A/B testing plans for machine learning models- Engaged with cutting-edge machine learning literature to inform technical strategies to projects going forward.

- Completed as part of Masters of Data Science Capstone unit led by Eun-Jung Holden- Examining Geochemical data to identify abundances of criticalminerals and potential endowment across Western Australia.- Applying current Geoscience literature into actionable MachineLearning methodology.- Leading group members (both F2F and remote) to achieveweekly goals and liaising with Senior Geologist to presentfindings.

- Investigating Machine Learning/Deep Learning (Tensorflow,Keras, Scikit-learn) techniques ability to interpolateMeteorological data.- Simulating sensor error dataset with NumPy.- Communicating findings to CTO/CEO.

- Reading and implementing methodologies from research papers related to Temporal data.- Building and visualising Graph data structures to represent Temporal patterns.- Dataset generation of Temporal data.- Benchmarking various Machine Learning models on generated data.

Working as a remote Data Scientist in an early stage start up.- Implementing a Resume Extracting algorithm using Natural Language Processing libraries (Python, SpaCy)- Implementing the automation of annotating textual data for a Neural Network model.