Job Description

Choose a workplace that empowers your impact.

Join a global workplace where employees thrive. One that embraces diversity of thought, expertise and experience. A place where you can personalize your employee journey to be -- and deliver -- your best.



We are a purpose-driven, dynamic and sustainable pension plan. An industry leading global investor with teams in Toronto to London, New York, Singapore, Sydney and other major cities across North America and Europe. We embody the values of our 640,000 members, placing their best interests at the heart of everything we do.



Join us to accelerate your growth & development, prioritize wellness, build connections, and support the communities where we live and work.



Don't just work anywhere -- come build tomorrow together with us.



Know someone at OMERS or Oxford Properties? Great! If you're referred, have them submit your name through Workday first. Then, watch for a unique link in your email to apply.



OMERS Total Portfolio Management manages the public markets investment portfolio consisting of fixed income (including government bonds, Inflation-Linked Bonds and public and private credit) and equities, including global public equities, intellectual property and structured investments. The team's approach is focused on generating current income from high-quality assets and generating a consistent capital return with a lower volatility than the broad markets. We also seek to develop strategic partnerships with best-in-class investors to access credit and other opportunities on a global basis. The OMERS Capital Markets' team is distinguished by its diversity in expertise and experience and its commitment to reliable, lower-risk performance. Our objective is to construct a well-diversified portfolio that will deliver long-term annualized returns in the 6 to 9% range.



As a member of the OMERS Total Portfolio Management (TPM) team in Toronto, reporting to the Director, Head of AI Labs in Portfolio Analytics, you will contribute and lead our transformation of how Capital Markets improves investment decision making through data driven insights.



As a AI/ML Engineer in AI Labs, Portfolio Analytics, at OMERS Total Portfolio Management, you will be part of a team that produces world-class AI/ML powered analytics and quantitative tools. The role provides the successful candidate with the opportunity to work closely with our different investment teams and the rest of TPM, where you will develop and grow your understanding of ML and quantitative methods and AI tools that drive the development of the systematic components of portfolio management.



As a member of the AI Lab, Portfolio Analytics team, you will be responsible for the following:

Build AI & ML solutions across a wide range of investment applications: Develop and implement AI models and algorithms to solve complex problems.

Contribute to problem solving & brainstorming discussions related to AI & ML

Deep dive into data intensive initiatives

Analyze and preprocess data to ensure quality and relevance for AI applications.

Develop AI evaluation frameworks:

Conduct experiments and evaluate the performance of AI models

Convert research code into streamlined, deployable production code that includes robust data pipelines

Owning the end-to-end analytics production cycle

Stay updated with the latest advancements in AI and machine learning technologies. Work closely with Data and Technology department to expand on our technology stack

Effectively communicating highly technical subject matters to investment team portfolio managers and collaborate with cross-functional investment teams to integrate AI solutions into existing systems

Required Qualifications:

Bachelor's or Master's degree in Computer Science, Software Engineering, Physics, Mathematics, or a related field

Up to five years of experience in AI engineering or related roles

Proficiency in programming/querying languages such as Python, SQL, or C++

Experience with Python machine learning libraries such as TensorFlow, PyTorch, XGBoost, or Scikit-learn

Strong analytical and problem-solving skills

Excellent communication and teamwork abilities

Preferred Qualifications:

Ability to design and develop clean coding solutions heavily focused on data science applications

Ability to hypothesize, experiment and discuss data science ideas

Knowledge of cloud platforms such as AWS, Azure, or Google Cloud

Familiarity with big data technologies like Hadoop or Spark

Experience with natural language processing (NLP) & generative models

Understanding of software development best practices and version control systems (GitHub)

Challenges and Opportunities:

The role involves using highly sensitive information for making investment decisions

The role involves research and experimentation and embracing the fail fast principle

Candidates must be able to prioritize multiple tasks to complete deliverables in a timely fashion

This posting is for an existing vacancy.

The expected salary range for this position is $100,000.00 - $130,000.00 per year.



You may also be eligible to receive an annual Incentive Award pursuant to our Short-term Incentive plan and our Long-Term Incentive plan (if applicable), and to participate in our group benefits and retirement plans - details on these elements of compensation are included within OMERS & Oxford offer letters.



As one of Canada's largest defined benefit pension plans, our people-first culture is at its best when our workforce reflects the communities where we live and work -- and the members we proudly serve.



From hire to retire, we are an equal opportunity employer committed to an inclusive, barrier-free recruitment and selection process that extends all the way through your employee experience. This sense of belonging and connection is cultivated up, down and across our global organization thanks to our vast network of Employee Resource Groups with executive leader sponsorship, our Purpose@Work committee and employee recognition programs.

Note:

OMERS uses artificial intelligence tools to assist in the recruitment process.