Role Description We’re building a new quantitative research team focused on pricing, market-making, and risk models for prediction markets. This is a highly hands-on role for someone who can operate end-to-end: - Data engineering - Research - Modeling - Close collaboration with traders across sports and non-sports event markets - A range of contract types, including single-outcome markets, player props, and parlays Responsibilities: - Build data foundation, transform raw data into pricing inputs - Research and develop quantitative pricing, market-making, and risk models across sports, non-sports, player props, parlays, and correlated markets - Model cross-market dependencies, correlations, and portfolio effects, especially for combinatorial products such as parlays - Partner closely with traders to improve pricing logic, market coverage, and trading performance - Build frameworks for backtesting, simulation, and model validation - Create tools to monitor model performance, calibration, P&L attribution, and live trading outcomes - Help define the tooling, workflow, and research standards for a new team Qualifications - Strong quantitative background in statistics, math, ML, economics, or a related field - Experience building models in trading, sports, betting, prediction markets, or similar domains - Strong Python/data skills and comfort owning data pipelines as well as modeling - Ability to move quickly from raw data to research insight to production-ready mode - High ownership, strong communication skills and comfortable with fast-paced high growth environment Requirements - $100,000 - $150,000 a year Company Description We may use artificial intelligence tools to analyze the content of your Resume/CV against the specific requirements for the position. The purpose is to support our recruitment team in reviewing applications more effectively. These tools assist our recruitment team in their evaluation of your application by providing recommendations, but they do not replace human judgment. Final hiring decisions are ultimately made by humans who consider the insights generated by the tools along with other relevant information. If you would like more details about how your personal information is processed, please contact us.

Role Description This position is for a Student Part-Time Professor to teach the course "Introduction to Translation" (TRA1301) at the University of Ottawa's Faculty of Arts, specifically within the School of Translation and Interpretation. - Course Description: Introduction to translation as practised professionally. - Dictionaries and documentation. - Exercises in preparation for translation. - Translation of a variety of simple texts from French into English. Course Schedule: - Monday: 11:30-13:00 - Thursday: 13:00-14:30 Qualifications - M.A. in Translation or an undergraduate diploma and experience in translation. - Experience in the teaching of translation or post-editing is an asset. Requirements - This course will be offered online. - An acceptable level of education and/or experience could be viewed as being equivalent to the educational required and/or demonstrated experience. - If you are invited to continue the selection process, please notify us of any adaptive measures you might require. - Employees are required under provincial law to successfully complete all mandatory legislated training. - The hiring process will be governed by the current APTPUO collective agreements. Benefits - Hourly Rate: Teaching: $239.47 (2024-2025). - These rates do not include vacation pay nor statutory pay. - These rates will be applied until a new collective agreement is ratified. Retro will be paid after the ratification.

Role Description qSpark is looking for a quantitative researcher with a genuine edge in signal research and market microstructure to join our team. This role demands someone who ships research into live strategies and is laser-focused on strategies that achieve high Sharpe in production. - Research, develop, and continuously improve quantitative trading signals across HFT and near-HFT timeframes, with a relentless focus on high Sharpe, deployable strategies. - Design and run rigorous backtests that honestly account for microstructure: spread costs, venue behavior, print filtering, adverse selection, and realistic fill modeling — with the explicit goal of performance that holds up in production, not just on paper. - Collaborate closely with portfolio managers and developers to bring validated research into production and improve live strategies. - Analyze live performance regularly — decompose P&L, identify regime shifts, attribute slippage — and feed findings back into active research. - Take on increasing levels of research leadership over time, including mentoring and setting the direction for research cycles. Qualifications - 2+ years of hands-on quantitative research or strategy development in an electronic trading environment, with demonstrated ability to produce high Sharpe strategies that survive live deployment. - Strong applied statistics: Performance estimation, overfitting stress-tests, non-stationarity, and test design under real market constraints. - Fluent in Python for research; C++ familiarity a genuine advantage. - Solid knowledge of market microstructure: TAQ data interpretation, venue differences, short-term price dynamics. - Track record of taking research across the line into live production — not just exploring. - Bachelor's or Master's degree in Mathematics, Statistics, Physics, Computer Science, Computational Finance, or a related quantitative field.

• Lead the scoping, direction, and planning of user research for complex projects • Oversee and execute user research activities across the business area • Collaborate closely across design disciplines and other functions • Create, evolve, and promote user research guidelines and standards • Coach, mentor, and give constructive feedback to colleagues