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Algorithmic Trading Jobs in Toronto, ON (NOW HIRING)

Come and see why Fortune magazine consistently ranks The Trade Desk among the best small-medium-sized workplaces globally. ABOUT THE ROLE Our team is responsible for developing algorithms that run in ...

Financial modeling and analytical algorithms * Assess technical trade-offs across build vs. buy, model selection, infrastructure, cost, latency, and risk. * Partner with platform, security, and data ...

Senior Full Stack Engineer

Toronto, ON ยท Hybrid

CA$120K - CA$145K/yr

This role will work closely with Trading, Operations, Compliance, Data, and Technology teams to ... algorithms is an asset. Technical Environment * Backend: C#, .NET / .NET Core, Python, Azure ...

Coordinate and manage all trades, materials, inspections, and commissioning. * Conduct and/or ... algorithmic tools in any stage of its hiring, recruiting, or candidate evaluation process.

Working closely with Capital Markets trading teams, you will help define project requirements ... Solid working knowledge of design patterns, data structures, algorithms, threading and concurrency ...

Working closely with Capital Markets trading teams, you will help define project requirements ... Solid working knowledge of design patterns, data structures, algorithms, threading and concurrency ...

... algorithm quality and accuracy), and build proactive monitoring, alerting, and investigation routines. Champion a structured backlog and iterative enhancement program, aligning stakeholders on trade ...

... algorithm quality and accuracy), and build proactive monitoring, alerting, and investigation routines. Champion a structured backlog and iterative enhancement program, aligning stakeholders on trade ...

Maintain Master Tender List and ensure up-to-date trade information, along with consistently in ... Our organization may use automated tools, including artificial intelligence (AI) or algorithm ...

Showing results 21-40

Algorithmic Trading information

What is algorithmic trading?

Algorithmic trading involves trading in equities, currencies, or other financial instruments using computer programs. A trading program uses an algorithm to calculate current market conditions. This trading method is automated, so the program buys or sells the financial instrument when the algorithm says that the market meets all the requirements for a profitable trade. To create an algorithm, you perform mathematical and statistical analysis, also known as quantitative analysis, on an exchange or equity. After creating an algorithm with defined trading rules, you test it using historical market data. While this is primarily a technical field, you also need an understanding of the market.

What is algorithmic trading?

Algorithmic trading refers to the use of computer programs and algorithms to automatically execute trading orders in financial markets. These algorithms follow predefined rules based on factors like price, timing, and volume to optimize trading strategies and reduce human intervention. Algorithmic trading is widely used by institutional investors, hedge funds, and individual traders to increase efficiency, minimize costs, and capitalize on market opportunities. It can range from simple rule-based systems to complex strategies involving machine learning and artificial intelligence.

What is the difference between Algorithmic Trading vs Quantitative Analyst?

AspectAlgorithmic TradingQuantitative Analyst
Required CredentialsDegree in finance, computer science, or related field; programming skillsDegree in mathematics, statistics, or finance; strong analytical skills
Work EnvironmentTrading firms, hedge funds, financial institutions; fast-pacedInvestment banks, asset management firms; research-focused
Employer & Industry UsageUsed to automate trading strategiesDevelops models to inform trading decisions

While both roles involve quantitative skills and finance knowledge, Algorithmic Traders focus on implementing automated trading systems, whereas Quantitative Analysts develop models and strategies that may be used by traders or firms. The roles often overlap but differ mainly in their primary focus: execution versus modeling.

What are the main challenges faced by professionals in algorithmic trading, and how can they be addressed?

Professionals in algorithmic trading often encounter challenges such as developing strategies that remain effective in rapidly changing markets, minimizing latency for faster execution, and managing the risks associated with automated trading systems. To address these challenges, it's essential to stay updated with the latest market trends and technological advancements, conduct rigorous backtesting of algorithms, and implement robust risk management protocols. Collaboration with quantitative analysts, software engineers, and risk managers is also key to ensuring strategies are both innovative and resilient.

Do algorithmic traders make money?

Algorithmic traders can make money by developing and implementing automated trading strategies that exploit market opportunities. Success depends on skills in programming, quantitative analysis, and risk management, and profitability varies based on strategy performance and market conditions. Not all algorithmic traders are profitable, and many face significant competition and operational costs.

What are the key skills and qualifications needed to thrive as an algorithmic trader, and why are they important?

To thrive as an Algorithmic Trader, you need a strong background in quantitative analysis, programming (often Python, C++, or Java), and a solid understanding of financial markets, typically supported by a degree in mathematics, engineering, finance, or computer science. Familiarity with statistical modeling tools, trading platforms, and backtesting systems is essential, and certifications such as CFA or FRM can be advantageous. Superior problem-solving skills, attention to detail, and the ability to work under pressure set standout professionals apart in this field. These skills are crucial to developing, implementing, and refining trading strategies that can operate profitably and reliably in fast-moving financial environments.

What are the most commonly searched types of Algorithmic Trading jobs in Toronto, ON?

The most popular types of Algorithmic Trading jobs in Toronto, ON are:

Infographic showing various Algorithmic Trading job openings in Toronto, ON as of August 2026, with employment types broken down into 92% Full Time, and 8% Part Time. Highlights an 92% In-person, and 8% Hybrid job distribution.

Applied Scientist II

The Trade Desk

Toronto, ON โ€ข On-site

Full-time

Posted 10 days ago


Job description

The Trade Desk is a global technology company with a mission to create a better, more open internet for everyone through principled, intelligent advertising. Handling overย 1 trillionย queries per day, our platform operates at an unprecedented scale. We have also built something even stronger and more valuable: an award-winning culture based on trust, ownership, empathy, and collaboration. We value the unique experiences and perspectives that each person brings to The Trade Desk, and we are committed to fostering inclusive spaces where everyone can bring their authentic selves to work every day.

Do you have a passion for solving hard problems at scale? Are you eager to join a dynamic, globally-connected team where your contributions will make a meaningful difference in building a better media ecosystem? Come and see why Fortune magazine consistently ranks The Trade Desk among the best small-medium-sized workplaces globally.

ABOUT THE ROLE

Our team is responsible for developing algorithms that run in our real time bidding platform with the ultimate goal of helping thousands of customers run effective advertising campaigns. Many of our problems involve getting deep learning algorithms to train and inference at scale. Some of the challenges include sparse and/or noisy labels and data, large output space (thousands of labels), millisecond latency requirements, high QPS requirements (thousands or millions per second), and unreliable ground truth.

Applied Scientists on our team work closely with engineering throughout the lifecycle of the product, from ideation to productionalization and monitoring.

Our Applied Scientists are end-to-end owners. You will participate actively in all aspects ofย designing, researching, building, and delivering data-focused products for our clients.

We are a team built on a foundation of generosity and openness, and we expect our data scientists to help make others better and raise the bar for those around them. As a Data Scientist, you will contribute meaningfully through leading others.

WHO WE ARE LOOKING FOR

  • You have a sustained track record of making significant,self-directed, and end-to-end contributions to complex, multi-layered, impactfulย machine learningย 
  • You think beyond just the task at hand to deeply understand the 'why' behind what you are doing and anticipate problems that may be several degrees removed from obvious requirements.
  • You make your team better by proactively advocating for and driving improvements not just to what the team produces, but also the ways in which the team works.
  • You have a strong sense of data intuition. At our scale, many off-the-shelf modeling techniques (open source and enterprise) simply don't work. You are able to work from first principles to develop solutions and adapt them to a unique environment.
  • You are a broadly skilled data scientist with deep expertise working with embedded models in always-onproduction systemsย and working across a variety of technologies and data sources.
  • You have a product-focused mindset. You have the passion and ability to contribute to the process of discovering what will delight our clients and push forward one of the world's largest and most influential industries toward a vision of openness, transparency, and evidence-based decision-making.
  • You work with confidence and without ego. Our data scientists have deep knowledge and exercise a high degree of leadership in their daily work. You have strongly-held, defensible ideas, and advocate for what you believe is right. You are also adept at identifying andevaluatingย trade-offs, willing to be proven wrong, and quick to support your fellow teammates.
  • You value, seek out, and foster diversity. We are a global team from many diverse backgrounds, with different experiences and perspectives. To complement this team, you will welcome ideas that are different from your own and be skilled at finding and building from common ground.
  • You are a creative thinker, not bound by "the way things have always been done." What you know is less important than how well you learn and innovate. We don't need data scientists who know all the answers; we need data scientists who can invent the answers no one has thought of yet, to the questions yet to be asked.
  • You have an abundance of intellectual curiosity and are enthusiastic to learn (and teach) new technologies / techniques.
  • You are you comfortable working on an agile, distributed team spanning multiple time zones and continents.
  • You are able to communicate effectively across both technical and non-technical audiences.

WHAT YOU BRING TO THE TABLE

We do not expect you to know every technology we use when you start at TTD. What we care most about is that you can learn quickly and solve complex problems using the best tools for the job. However, we find that the most successful candidates typically come in with something like the following experience:

  • BS/MS in Mathematics, Computer Science, Statistics, or a related quantitative field with 3+ years of full-time industry experience, or a PhD with 2+ years of full-time industry experience, in a role that involves bringing products from ideation to production.
  • Experience in deep learning, Pytoch preferred.
  • Experience running heavy workloads on a distributed computing cluster (especially EMR or Databricks), leveraging technologies like Spark to work with large datasets is preferredย 
  • Proficient in Python, Scala, and SQL; hands-on experience with Spark, EMR and Ray.
  • Proficient in software engineering concepts like containerization and version control is preferred.ย 
  • Experience with ads, recommendation systems or KPI optimization modeling is a plus; large-scale advertising platforms and lift modeling (conversion lift, brand lift) experience strongly preferred.