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Algorithmic Trading Developer Jobs in Toronto, ON

Financial modeling and analytical algorithms * Assess technical trade-offs across build vs. buy ... Degree in Mathematics, Computer Science, Engineering, Physical Sciences, or a related quantitative ...

Intermediate Software Engineer

Toronto, ON · On-site

CA$80K - CA$105K/yr

... trade-offs. · A sound understanding of algorithmic complexity, general system architecture, and source control. · A Bachelor's degree in Computer Science or Engineering is strongly preferred. Nice ...

Great at algorithm design, problem solving, and complexity analysis * Ability to work independently ... Familiarity with any of the following: digital wallets, clearing and settlement, lending, trading ...

... algorithms, statistical model development and related technologies. The Data Scientist also ... trade-offs, and success measures * Proficient programming skills in Python, R, Spark, or other open ...

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Algorithmic Trading Developer information

What are the key skills and qualifications needed to thrive as an Algorithmic Trading Developer, and why are they important?

To thrive as an Algorithmic Trading Developer, you need strong programming skills (especially in Python, C++, or Java), a solid grasp of quantitative finance, and typically a degree in computer science, mathematics, or related fields. Expertise in trading platforms, financial data APIs, and familiarity with machine learning libraries and version control systems is common, alongside relevant certifications like CFA or CQF. Exceptional analytical thinking, attention to detail, and problem-solving abilities are crucial soft skills for success in this role. These capabilities enable developers to design, test, and optimize trading algorithms that perform reliably and profitably in dynamic financial markets.

What is the difference between Algorithmic Trading Developer vs Quantitative Analyst?

AspectAlgorithmic Trading DeveloperQuantitative Analyst
Required CredentialsBachelor's or Master's in Computer Science, Finance, or related fields; programming skillsDegree in Mathematics, Statistics, Finance, or Economics; strong analytical skills
Work EnvironmentDevelops trading algorithms, codes, tests, and implements trading systemsBuilds models, analyzes data, and provides trading insights
Employer & Industry UsageFinancial firms, hedge funds, trading firms focusing on system developmentInvestment banks, hedge funds, asset managers focusing on data analysis

While both roles work within the finance industry and require quantitative skills, Algorithmic Trading Developers primarily focus on coding and implementing trading algorithms, whereas Quantitative Analysts analyze data and develop models to inform trading strategies.

What is an Algorithmic Trading Developer?

An Algorithmic Trading Developer is a software professional who designs, builds, and maintains computer programs (algorithms) that automatically execute trades in financial markets. These developers combine knowledge of programming, quantitative analysis, and financial markets to create strategies that can analyze market data and place trades faster and more efficiently than human traders. Their work often involves working with large datasets, optimizing code for speed, and ensuring low-latency execution. They may also collaborate with traders and quantitative analysts to implement and test new trading ideas.

What are some common challenges Algorithmic Trading Developers face when deploying new trading strategies?

Algorithmic Trading Developers often encounter challenges such as ensuring low-latency execution, accurately backtesting strategies with real-world data, and managing the risks of live trading. Deployment requires close collaboration with quantitative analysts, traders, and IT teams to validate models and maintain robust infrastructure. Additionally, developers must monitor for unexpected market conditions and quickly address any system failures or regulatory changes that could impact trading performance.
What are popular job titles related to Algorithmic Trading Developer jobs in Toronto, ON? For Algorithmic Trading Developer jobs in Toronto, ON, the most frequently searched job titles are:
What job categories do people searching Algorithmic Trading Developer jobs in Toronto, ON look for? The top searched job categories for Algorithmic Trading Developer jobs in Toronto, ON are:
Infographic showing various Algorithmic Trading Developer job openings in Toronto, ON as of June 2026, with employment types broken down into 94% Full Time, 5% Part Time, and 1% Contract. Highlights an 82% Physical, 6% Hybrid, and 12% Remote job distribution.
Staff Data Scientist

Other

Posted 27 days ago


Job description

About Clutch:

Clutch is Canada's largest online used car retailer, delivering a seamless, hassle-free car-buying experience to drivers everywhere. Customers can browse hundreds of cars from the comfort of their home, get the right one delivered to their door, and enjoy peace of mind with our 10-Day Money-Back Guarantee... and that's just the beginning.

Named one of Canada's top growing Companies two years in a row and also awarded a spot on LinkedIn's Top Canadian Startups list, we're looking to add curious, hard-working, and driven individuals to our growing team.

Headquartered in Toronto, Clutch was founded in 2017. Clutch is backed by a number of world-class investors, including Canaan, BrandProject, Real Ventures, D1 Capital, and Upper90. To learn more, visit clutch.ca.

About the role:

Clutch is hiring a Staff Data Scientist to lead major improvements to our pricing algorithms and applied machine learning systems.

This is a high-ownership role for someone who thrives in ambiguity, can go deep on research and modeling, and has a track record of deploying ML to production with measurable business impact. You'll work on ML systems that already drive real outcomes - including pricing models that purchase >$1M of vehicles per day with no human intervention - with significant opportunity to take them to the next level as we scale.

You'll join a small, high-leverage data team where your work will be visible, measurable, and business-critical, with the chance to expand into additional high-impact ML domains like lending, logistics optimization, fraud detection, and recommendations. In this role, you'll own problem areas end-to-end from identifying opportunities and shaping the approach, to shipping production models and driving measurable improvements in margin and conversion.

What you'll do:

  • Own and drive improvements to Clutch's pricing algorithms, balancing margin, conversion, and customer experience.
  • Deep-dive into market and vehicle data to identify the key relationships between vehicle attributes, market dynamics, and pricing outcomes.
  • Build, validate, and deploy ML models and algorithms into production - and iterate quickly based on real-world performance.
  • Lead feature engineering, model evaluation, and experimentation design.
  • Partner with Product, Engineering, Strategy & Ops, Sell-To-Clutch & Retail to prioritize the highest-impact opportunities.
  • Contribute to additional applied ML domains as needed, including:
    • Financing / lending decisioning
    • Fraud detection
    • Search and discovery optimization
    • Vehicle recommendations / personalization

What we're looking for:

  • 8+ Years of Experience: A proven track record as a Data Scientist, with a history of delivering measurable business impact through machine learning.
  • 0-to-1 Strategic Autonomy: Proven ability to navigate high-ambiguity environments. You own the roadmap by evaluating the data, identifying untapped opportunities, and formulating your own research theories.
  • End-to-End Technical Ownership: Deep Python proficiency with the ability to own the entire lifecycle: from raw data exploration and feature engineering to model architecture and production deployment.
  • Production-Grade ML: Strong experience building and deploying traditional ML algorithms into live environments, ensuring they are robust, scalable, and maintainable.
  • Foundational Rigor: Strong statistical fundamentals and a disciplined approach to validation. You ensure that every model is built on a foundation of sound logic and clean data, maintaining high standards for accuracy and reliability without external oversight.
  • Excellent Communication: Able to bridge the gap between complex technical findings and business ROI. You can distill "black box" complexity into clear trade-offs and actionable recommendations for business leaders.