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
- 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
- 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
- Podcast guest for the Mentorship podcast (coming soon)