Shiva
Soleimany
Welcome to Shiva Soleimany's homepage.
I'm a data scientist and machine learning engineer currently
currently engaged in research at TMU. Previously, I worked as a data scientist at Offerland
and as a data engineer at Borealis AI. Explore my site to learn more about my projects and professional journey.
Hey there, I'm Shiva.
I specialize in data processing, building ML and statistical models, and crafting impactful visualisations to articulate insights effectively. To look out for my experiences in each of these areas with examples, check out the services section.
I earned my Master of Science in Computing Science from the University of Alberta, specializing in Transfer Learning in Reinforcement Learning under the supervision of Professor Matthew Taylor at the RLAI lab.
My career started as a Data Engineer at Borealis AI, where I managed and optimized multiple scalable data pipelines, some handling over 15 million records. I automated data ingestion processes, implemented robust ETL operations, and ensured seamless migration of integration tests when upgrading the infrastructures. I also worked closely with the RBC Brain team to develop an advanced machine learning platform that facilitated streamlined model development and accelerated innovation within our research environments.
I then transitioned to a Data Scientist role at Offerland, where I developed a statistical model for predicting real estate rental and sales prices across Canada. I was responsible for the entire project lifecycle, from defining evaluation metrics for a “good” model with product and business teams to final deployment. I used tools like Tableau, Plotly Express, and Streamlit to create dynamic visualizations that helped stakeholders make data-driven decisions. This role allowed me to refine my skills in data manipulation, analysis, and model tuning, directly influencing business strategies and outcomes.
My work with prompt engineering in Data Science sparked my interest in Large Language Models (LLMs), motivating me to dive deeper into this field. To build on this passion, I transitioned to a researcher role, where I both got to use my experience in RL and also gain expertise in LLMs. I worked on projects involving prompt reformulation to improve LLM performance and leveraging reinforcement learning (RL) techniques to develop few-shot re-rankers.
Now, having explored both the theoretical and practical aspects of data science and machine learning, I’m eager to bring these insights back to the industry. My research-driven approach and practical experience uniquely position me to deliver high-impact results and drive innovation in a fast-paced, solution-oriented environment.
Twitter GithubServices
Book Summaries
How to apply various statistical methods to data science and avoid their misuse.
VisitA guide to making visualizations that accurately reflect the data, tell a story, and look professional.
Coming soonBest practices and solutions to recurring problems in machine learning.
Coming soon