Why you’ll love working here:
high-performance, people-focused culture
our commitment that equity, diversity, and inclusion are fundamental to our work environment and business success, which helps employees feel valued and empowered to be their authentic selves
learning and development initiatives, including workshops, Speaker Series events and access to LinkedIn Learning, that support employees’ career growth
membership in HOOPP’s world class defined benefit pension plan, which can serve as an important part of your retirement security
competitive, 100% company-paid extended health and dental benefits for permanent employees, including coverage supporting our team's diversity and mental health (e.g., gender affirmation, fertility and drug treatment, psychological support benefits of $2,500 per year, and newly extended maternity/parental leave top of 26 weeks)
optional post-retirement health and dental benefits subsidized at 50%
yoga classes, meditation workshops, nutritional consultations, and wellness seminars
access to an annual wellness reimbursement program for health and wellness-related expenses for permanent and temporary employees
the opportunity to make a difference and help take care of those who care for us, by providing a financially secure retirement for Ontario healthcare workers
Job Summary
The AI Engineer will play a crucial role in developing and deploying AI/ML solutions for various applications in investment management at HOOPP. The successful incumbent will work closely with cross-functional teams to understand requirements, prototype solutions, and deploy models into production environments. Your expertise in data analysis, AI/ML modeling, and quantitative modeling will drive our innovation and success.
What you will do:
Data Analysis & Processing
Evaluate and clean data sets from various sources (SQL databases, NoSQL databases, graph databases, documents, corpora) to ensure they are ready for AI/ML modeling.
Proficiency in data preprocessing techniques such as handling missing data, outlier detection, normalization, and transformation to ensure data readiness for modeling.
Integrate and merge disparate datasets to create unified datasets suitable for AI/ML modeling.
AI/ML and Quantitative Modeling
Develop custom AI/ML or statistical models tailored to specific use cases in investment management.
Evaluate, fine-tune, and deploy open-source AI/ML models for various applications in investment management.
Experience with several of the following frameworks: Pandas, NumPy, SKLearn, XGBoost, PyTorch, TensorFlow, Keras.
Prototyping & Deployment
Proficiency in prototyping lightweight AI/ML solutions to quickly validate hypotheses and demonstrate feasibility.
Develop wrapper APIs for model integration and interaction with other systems.
Integrate AI/ML models with existing systems and databases, ensuring seamless functionality and performance.
Performance Improvement
Monitor the performance of AI/ML models and make adjustments to improve accuracy and efficiency.
Identify and design performance metrics for models to monitor, improve, and adjust models to maintain or enhance accuracy, efficiency, and reliability.
Product Discovery
Collaborate with business stakeholders, particularly Investment and Risk Management teams, to scope out modeling requirements and success metrics.
Research & Innovation
Stay up-to-date with the latest research and advances in AI/ML and its applications to investment management.
Apply innovative techniques to a variety of use cases in investment management.
Commitment to staying updated with the latest research trends, advancements, and best practices in AI/ML relevant to investment management.
Apply innovative AI/ML techniques and approaches to solve complex challenges and explore new opportunities in investment management.
Collaboration
Experience working collaboratively with data engineers, software engineers, and teams in investment management and risk management to develop AI/ML solutions.
Strong interpersonal skills and ability to work effectively in multidisciplinary teams, contributing to shared goals and outcomes.
Communication
Clear and concise communication skills to articulate complex AI/ML concepts, methodologies, and results to non-technical stakeholders and team members.
Capability to present findings, insights, and recommendations from AI/ML experiments in a compelling and understandable manner.
Documentation
Document AI/ML experiments, methodologies, findings, and insights systematically for future reference and knowledge sharing.
Our Technology Stack
Programming Languages: Python (primary for AI/ML), SQL (data querying), and/or proficiency in some object-oriented language.
AI/ML Frameworks and Libraries: TensorFlow, PyTorch, Keras, Scikit-learn, XGBoost, Pandas, NumPy.
Data Storage and Management: SQL Databases (MySQL, PostgreSQL, MS SQL Server, Snowflake) and NoSQL Databases (MongoDB, Cassandra).
Cloud Platforms and Big Data: AWS (EC2, S3, Lambda, SageMaker, Glue, Athena).
Containerization, Orchestration, and Development Tools: Docker, Kubernetes, Flask, FastAPI, Django, GitHub, GitHub Actions, MS DevOps,, Jupyter Notebooks, Anaconda, VSCode/PyCharm.
What you bring:
A master's or Ph.D. in a quantitative field.
9+ years of overall experience including grad school.
5+ years of experience in building production-level AI/ML and other quantitative models.
Strong foundation in statistics, optimization, NLP, and AI/ML algorithms, with a specialization in at least one of the areas.
Experience in investment management or related financial domains is preferred.
Proficiency in Python, R, or an object-oriented programming language.
Familiarity with distributed computing tools and cloud platforms (AWS, Azure, GCP).
Excellent problem-solving skills and ability to think creatively.
Strong interpersonal skills and ability to work effectively in a collaborative team environment.