I am a Data Scientist at Amazon with more than 4 years of professional experience. I studied Applied Statistics and Finance at York University and the Schulich School of Business, focusing on applied mathematics and information science. I develop tools for data extraction from legal/financial documents and invoices and build agentic systems to support financial analysis.

Data extraction from legal and financial documents/invoices, agentic systems for financial analysis, NLP (NER & relation extraction & text labeling), statistical modeling & machine learning, AWS/SageMaker, Python & SQL, data visualization.

Data extraction from legal/financial documents/invoices, agentic systems for financial analysis

Hussain Abbas is the Principal Engineer at Axelerant, bringing over 20 years of programming experience and more than a decade in engineering leadership. He has a strong background in web development and has worked with various technologies including Python, Laravel, Symfony, AngularJS, and Vue.js. Hussain is passionate about mentoring his team, fostering a productive and happy work environment. He actively participates in community meetups and contributes to the Drupal community, having presented at numerous conferences.

DevOps, Machine Learning, Data Science, Python, Laravel, Symfony, Angular, Vue.js, open source contributions, engineering team productivity, AI for coding.

Leading Engineering efforts at Axelerant for our customers. Actively implement newer AI-based techniques and platforms using AI.

Curious, driven, and passionate about solving real-world problems with data. I recently completed my Master’s in Data Science & Analytics from Toronto Metropolitan University and bring hands-on experience across the data stack — from A/B testing and predictive modeling to GenAI applications and dashboarding. I've worked on customer engagement models at Manulife, built real-time marketing dashboards and uplift strategies at Clearoute, and shipped full-stack ML and AI solutions that automate everything from resume matching to video transcription and rewriting. Open to roles in Data Science, Analytics/Business Intelligence, or ML Engineering — especially where I can blend experimentation, product thinking, and scalable impact. 📊 Tools: Python, SQL Github: https://github.com/Muzammil-Elahi

I'm interested in decision science, Fintech, Quant. No particular field but more so how AI/Ml is being used for example process optimization or automation.

Currently, I'm building an AI-powered video editing/publishing platform to streamline content creation.

Rafael Madrigal is a Senior Data Scientist at Manulife with over eight years of experience in analytics, specializing in Natural Language Processing, Responsible AI, and Applied Machine Learning Research. He has a strong track record of delivering innovative ML solutions for complex challenges in banking and finance. Previously at GCash (Ant Financial), he led experimental Generative AI projects, focusing on Retrieval Augmented Generation, Text-to-SQL, and LLM operations, while also spearheading AI fairness research and co-founding the company’s AI Innovations group. Rafael holds a Master’s degree in Data Science and is passionate about leveraging AI for positive change, actively seeking contract work and mentorship opportunities.

Generative AI, Artificial Intelligence, AI Ethics

Leading advanced NLP and Responsible AI initiatives at Manulife, focusing on applied machine learning research for banking and finance challenges. Driving projects involving Generative AI techniques such as Retrieval Augmented Generation, Text-to-SQL, and LLM operations. Spearheading AI fairness research and developing policies to ensure model explainability and fairness. Managing end-to-end AI deployments and building scalable ML pipelines.

I am a motivated Project Manager with a background in technology and business, passionate about driving innovative projects from concept to completion. With experience in product development, and data analysis, I excel in coordinating teams, optimizing workflows, and delivering impactful solutions. Beyond work, I enjoy running, coding, and exploring creative ways to integrate AI into everyday challenges. And electrical engineering

am deeply interested in exploring AI applications, particularly in natural language processing (NLP), machine learning models, and computer vision. I am focused on learning more about deploying AI in cybersecurity, automation, and data-driven decision-making.

I’m currently experimenting with a data analysis and visualization tool using Python. The tool integrates the Google Sheets API for real-time data retrieval and employs OpenAI’s GPT models for advanced data insights. A key feature is its interactive dashboard built in Notion, enabling seamless collaboration and real-time updates. I’m also exploring a TensorFlow-based computer vision project to fine-tune image classification models. This involves dataset augmentation, model training, and deploying the solution in interactive environments like Telegram bots to make AI capabilities more accessible. Both projects are designed to enhance automation and data-driven decision-making, and while they’re still in development, I’m happy to share more details about the technologies and methodologies used.

Safa Nasir is a Sr Data Engineer at Citi.

Building LLama2-RHLF for fine-tuning LLaMA2 with RLHF-style reward modeling, and implementing a vision-transformer-from-scratch in Python.