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Employment type in the employer’s posting: Full timeFrom the employer’s posting
Source, build, clean and maintain datasets for investment analyses
Design data extraction and processing pipelines for messy raw sources
Run statistical and ML analysis to help deal teams validate investment theses
Turn ad-hoc analyses into dashboards and reusable tools for deal teams
Own discrete analyses end-to-end, from data sourcing to stakeholder output
Source, build, clean, and maintain datasets that feed Alpha Science's analyses and tools
Design and build data extraction and processing pipelines that turn raw, often messy sources
Conduct hands-on analysis and statistical / ML modeling to draw out insights that help deal teams pressure-test, challenge, or validate their investment theses
Partner with senior team members to turn ad-hoc analyses into dashboards, visualizations, and reusable tools for deal teams
Take ownership of discrete analyses and projects end-to-end, from data sourcing through to the final stakeholder-ready output
Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or any equivalent quantitative degree from a recognized university
Proficiency in English and French (or willingness to learn) (frequent interactions in English with PSP employees based in our offices in Hong Kong, London and New York, and interactions in French with employees in our local offices in Montreal and Ottawa)
Experience extracting and structuring data from varied sources (documents, filings, APIs, vendor feeds) is a strong asset
Experience working with cloud / lakehouse data platforms (e.g., Databricks, Snowflake) is an asset
Practical experience applying statistical or machine learning techniques to real-world, often messy datasets
Experience building dashboards or visualizations (e.g., Power BI, Tableau, or Python-based visualization libraries)
WHAT YOU’LL NEED - Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or any equivalent quantitative degree from a recognized university - Two to three (2-3) years of experience in data science, data engineering, or quantitative analytics, ideally within financial services, investing, or another analytically intensive environment
We’re one of Canada’s largest pension investors, with CAD$320.6 billion of net assets as of March 31, 2026.
In the employer’s words · Read in context
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Day 83
Up longer than most. Similar jobs usually come down between day 7 and day 55.
From 10,000 similar postings that came down. Coming down is not always a hire.
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- Two to three (2-3) years of experience in data science, data engineering, or quantitative analytics, ideally within financial services, investing, or another analytically intensive environment - Strong hands-on proficiency in Python and SQL for data extraction, ingestion, wrangling, analysis, and modeling - Experience extracting and structuring data from varied sources (documents, filings, APIs, vendor feeds) is a strong asset
- Practical experience applying statistical or machine learning techniques to real-world, often messy datasets - Experience building dashboards or visualizations (e.g., Power BI, Tableau, or Python-based visualization libraries) - Experience working with alternative or unconventional datasets (e.g., geolocation, foot traffic, satellite, or pricing data) is an asset
- Experience extracting and structuring data from varied sources (documents, filings, APIs, vendor feeds) is a strong asset - Experience working with cloud / lakehouse data platforms (e.g., Databricks, Snowflake) is an asset - Practical experience applying statistical or machine learning techniques to real-world, often messy datasets
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We file this job under Data Scientist, by the work the posting describes.
Data scientists use statistics and models to answer harder questions, such as whether a change worked, what demand will look like next quarter, or which customers are likely to leave.
Postings usually ask for Python or R, SQL, statistics, experiment design and common machine learning methods.
Unlike an ML engineer, a data scientist's main output is often an analysis or a first model, not software running inside a product.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-2051.
We’re one of Canada’s largest pension investors, with CAD$320.6 billion of net assets as of March 31, 2026.
We invest funds for the pension plans of the federal public service, the Canadian Forces, the Royal Canadian Mounted Police and the Reserve Force. Headquartered in Ottawa, PSP Investments has its principal business office in Montréal and offices in New York, London and Hong Kong.
Capturing and leading complex global investments requires us to work as one to seize valuable opportunities, in close collaboration with some of the world’s top companies. At PSP Investments, you’ll join a team of motivated and engaged professionals, dedicated to propelling our organization further than ever before.
As a Data Scientist, Data Science and Artificial Intelligence, you will play a hands-on role in harnessing data science to facilitate the generation of alpha for PSP. To this end, you will:
Work within a ten-person team, Alpha Science, dedicated to supporting investment decisions with AI across the whole pension fund and associated entities
Source, build, clean, and maintain datasets that feed Alpha Science's analyses and tools, including market data, alternative data (geolocation, foot traffic, satellite, pricing) and portfolio company data
Design and build data extraction and processing pipelines that turn raw, often messy sources, including documents, filings, and vendor feeds, into analysis-ready datasets
Conduct hands-on analysis and statistical / ML modeling to draw out insights that help deal teams pressure-test, challenge, or validate their investment theses
Partner with senior team members to turn ad-hoc analyses into dashboards, visualizations, and reusable tools for deal teams across PSP's asset classes
Develop a working knowledge of the sectors and datasets within your coverage, building the judgment to identify the questions and analyses most relevant to deal teams' decision-making
Prepare clear, well-structured materials that tell a compelling story with the data for both technical and non-technical stakeholders
Support senior team members in presenting findings directly to deal teams and, where relevant, portfolio company management
Contribute to and leverage the team's broader toolkit, including its LLM-powered tools, as relevant to the projects you're working on
Take ownership of discrete analyses and projects end-to-end, from data sourcing through to the final stakeholder-ready output
Bachelor's or Master’s degree in Data Science, Computer Science, Statistics, Engineering, Applied Mathematics, or any equivalent quantitative degree from a recognized university
Two to three (2-3) years of experience in data science, data engineering, or quantitative analytics, ideally within financial services, investing, or another analytically intensive environment
Strong hands-on proficiency in Python and SQL for data extraction, ingestion, wrangling, analysis, and modeling
Experience extracting and structuring data from varied sources (documents, filings, APIs, vendor feeds) is a strong asset
Experience working with cloud / lakehouse data platforms (e.g., Databricks, Snowflake) is an asset
Practical experience applying statistical or machine learning techniques to real-world, often messy datasets
Experience building dashboards or visualizations (e.g., Power BI, Tableau, or Python-based visualization libraries)
Experience working with alternative or unconventional datasets (e.g., geolocation, foot traffic, satellite, or pricing data) is an asset
Prior exposure to investment, and deals in Public Markets, Real Estate or Natural Resources is an asset
Resourceful and creative, with demonstrated ability to work in a fast-paced environment with shifting priorities
Highly effective written and verbal communication
Proficiency in English and French (or willingness to learn) (frequent interactions in English with PSP employees based in our offices in Hong Kong, London and New York, and interactions in French with employees in our local offices in Montreal and Ottawa)
We offer a tailored employee experience and competitive total rewards and benefits package* designed to attract and retain global diverse talent, reward performance, and reinforce business strategies and priorities. Beyond salary and incentive pay eligibility, you have access to:
*Benefits package may vary based on your employee type.
At PSP Investments, we aim to provide a workplace where everyone feels valued, safe, respected and empowered to grow. As part of this leadership commitment, we strongly encourage applications from all qualified applicants and strive to offer an inclusive and accessible candidate experience. If you require any accommodation for any part of the recruitment process, please let us know.
Visit us on www.investpsp.com/en/
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Montreal
- Vacation days available on day one with additional days on milestone service anniversaries, and summer Friday afternoons off - A hybrid work model that includes four in-office days, Monday through Thursday - The opportunity to work temporarily from another location in accordance with the applicable policy
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