Semantic Health - ML Lead (2021-2022)

• Spearheaded the research and development of the company's clinical ML offerings (variety of clinical NLP + OCR + Tabular), successfully delivering multiple client deployments, leading to a US expansion, and driving a $60M valuation.
• Hired and led a team of 7 ML Scientist / Engineers: Managed their professional growth, setting objectives, conducting standups, and nurturing their leadership and technical capabilities.
• Defined Hiring Policies and Interview Processes: Established effective hiring policies, interview questions, scoring systems, and take-home assignments to identify top talent for ML, Data Engineering, and Backend roles.
• Created the long term ML product vision of the company
• Re-wrote the initial Python stack reducing technical debt and allowing for new hire onboarding without big issues. Introduced unified code, data, and testing standards
• Created and deployed multiple new ML product lines based on customer calls and product insights from internal QA efforts leading to increases of 100k ARR per contract
• Introduced a novel non-intrusive data labelling and feedback scheme, increasing the granularity of labels without impacting the end user's workflow, which led to creation of new models with >50% performance gain on downstream KPIs
• Created weekly progress reports and presented at all-hands and led customer calls
• Created extensive model and prediction fallbacks allowing us to eliminate the need for on-call support on the ML side
• Contributed to Open source libraries supporting US Military veteran care efforts. Named the first outside collaborator to the library.
• Contributed optimizations to multiple NLP libraries allowing for parallelization of computation (Spacy extensions + Srsly)
• Supervised research projects leading to multiple scientific publications and product improvements
• Created an active learning approach allowing to improve data labelling efforts reducing costs by >75%
• Led the MLOps + DevOps efforts to integrate ML into dev and staging cloud environments allowing for end-to-end model testing for releases which allowed the team to move away from on-prem testing
• Led code review and tech debt improvement sprints across the entire python stack
• Designed and implemented APIs for vending model predictions
• Roadmapped and oversaw a new MLOps initiative to allow for better data and model monitoring (Feast + MLFlow + Seldon)
• Led internal R&D efforts on new modelling schemes to improve the existing products

UofT - Teaching (2020-2023)

• Developed comprehensive educational materials and delivered engaging instruction for a range of undergraduate and graduate courses in Statistics and Machine Learning.
• Communicated sophisticated technical topics to a non-technical audience
• Modernized the CSC412 - Probablistic ML course curriculum by adding Attention, Transformers, and Diffusion models

Semantic Health - Machine Learning Scientist (2019-2020)

• Joined as the first employee - architected and built the ML stack setting the foundation for the company's NLP capabilities
• Developed Transformer-based models consistently surpassing the State-of-the-Art performance on a complex, massively multi-label dataset with significant labeling noise.
• Successfully deployed models to production, initially through Python scripts and database uploads, later optimizing the process by transitioning to Airflow, leading to first long term customer contracts
• Designed and implemented a diverse stack of models, spanning from basic keyword matching to advanced Deep Learning techniques.
• Pioneered a novel architecture for generating supporting evidence for ML model predictions, implemented and deployed it leading to a 300% increase in business KPIs for the product
• Developed a custom multi-armed bandit model deployment scheme eliminating guesswork and quadrupling ML deployment velocity to production (monthly to weekly model release cadence)
• Engaged with customers and gathered feedback to iterate on model improvements, ensuring alignment with customer needs and expectations.
• Conducted ML feature discovery calls with end users, resulting in the development of simple ML models that significantly enhanced product performance
• Established a custom quality assurance process for on-premises model deployments in a secure environment, guaranteeing model reliability and performance.
• Authored a series of Architecture Decision Records that still serve as the model for decision documents for the company
• Actively contributed to the company's culture by hosting weekly game sessions and fostering a positive and engaging atmosphere in meetings.
• Implemented a comprehensive testing suite for the ML stack, including model minification allowing for pre-commit end-to-end ML testing
• Planned and conducted data and label quality experiments identifying problems, addressed those by creating a novel, ontology-graph based label smoothing algorithm increasing model performance by 35%
• Found and solved a major bug in a huge open-source framework (AllenNLP)

UofT - Masters (2019-2022)

• Part time masters while working a full time (early stage startup) job
• A average (only one course not A+) in challenging courses
• Actively engaged in reasearch leading to Publications and a Research Visitor position at St Michaels hospital in Toronto
• Recognized for expertise, was offered to teach my own cohort as a sessional lecturer in Machine Learning

UofT - Bachelors (2015-2019)

• President Stats Union - brought it from a dead union (won with 14 total votes for a president of a union with ~6000 members) to multiple yearly events with attendance in the hundreds
• Took on teaching assistant (TA) responsibilities for upper-level courses while still an undergraduate, including instructing students in my own year.
• 4.0 GPA in stats courses
• Allowed to take One-on-One reading courses under famous professors (Dan Simpson, David Duvenaud)
• Actuarial Students National Association (ASNA) leadership from second year - held Director positions for Operations, Events, and Case competition
• Conducted ML research at RiskLab

Deloitte (2018)

• Authored a pratice-wide internal report on the state and future of AI in insurance
• Participated in reserving audits for a number of large auto insurance customers
• Assumed a client-facing role by working on-site for nearly 8 months, collaborating closely with a major auto-insurance client to fulfill their data engineering needs, assisting in reconciling their extensive book of business.
• Contributed to creation of a report for the Ministry of Finance on the state of Auto Insurance in Canada

Intact (2017)

• First year intern building a relatively large data project on my own (EoE)
• Built GLM auto insurance pricing models for regulators

Family business (2023)

• Built an english version of the site (WP)
• Done Business dev efforts to expand the company to North America

Misc

• Podcast guest for the Mentorship podcast (coming soon)