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Data Science Jobs in Ontario (NOW HIRING)

Title and Summary Director, Data Science Overview The Security Solutions Data Science team is responsible for delivering Artificial Intelligence (AI) and Machine Learning (ML) models that support ...

Manager, Data Science

Toronto, ON · On-site

CA$150K - CA$170K/yr

As a Manager, Data Science , you will lead a diverse team with exposure to different business partners and direct influence on future products and innovative solutions. You will lead a hungry team of ...

New

Manager, Data Science

Markham, ON · On-site

CA$150K - CA$170K/yr

As a Manager, Data Science , you will lead a diverse team with exposure to different business partners and direct influence on future products and innovative solutions. You will lead a hungry team of ...

New

The role: We're looking for a Director, Data Science/ML who will drive CookUnity's next phase of product innovation through forward-looking data science capabilities. This role goes beyond ...

The role: We're looking for a Director, Data Science/ML who will drive CookUnity's next phase of product innovation through forward-looking data science capabilities. This role goes beyond ...

Senior Manager, Data Science

Toronto, ON · On-site

CA$120K - CA$150K/yr

The Senior Manager, Data Science is responsible for the design and validation of advanced analytics methodologies at Numeris. This role leads the review and application of machine learning ...

Manager, Data Science

Toronto, ON · Hybrid

CA$82K - CA$154K/yr

Data Analytics & Reporting This is a hybrid role in Toronto Uses advanced analytical algorithms and technologies (e.g. machine learning, deep learning, artificial intelligence) to mine and analyze ...

Machine learning, data science, AI engineering, or applied analytics experience in industry or an academic setting. * Experience in mathematical and statistical model development to support ...

Effectively communicate the analytics approach and data science lifecycle with leadership and business partners. * Advocate and educate on the value of data driven decision making focusing on the ...

Data Science Engineer (GCP)

Toronto, ON · On-site

$90 - $120/hr

The Data Science Engineer (GCP) will play a key role at Stacktics Inc., where we design, create, deploy, maintain and grow industry-leading Cloud Infrastructure, Big Data Analytics and Cloud For ...

You will support data science and analytics efforts across multiple areas of the business including Sales, Marketing, Finance, HR, and related functions. You will contribute to building and improving ...

As part of a small Data Science team within a publicly traded, product-led company, you will help shape how data science influences product decisions, campaign outcomes, and business performance.

The role will be responsible for delivering high quality data science models, and the logistical challenges around improving a profitable service. You'll also be working on a diverse range of supply ...

The role will be responsible for delivering high quality data science models, and the logistical challenges around improving a profitable service. You'll also be working on a diverse range of supply ...

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Showing results 1-20

Data Science information

See Ontario salary details

$23.5K

$116.9K

$210.5K

How much do data science jobs pay per year?

As of Jul 17, 2026, the average yearly pay for data science in Ontario is $116,864.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,000.00 and $161,000.00 per year, depending on experience, location, and employer.

Is data science a good career?

Data science is a growing field with high demand for professionals skilled in statistics, programming, and data analysis tools like Python and R. It offers competitive salaries, diverse industry applications, and opportunities for advancement, making it a strong career choice for those with relevant skills and education.

What are the key skills and qualifications needed to thrive as a Data Scientist, and why are they important?

To thrive as a Data Scientist, you need a strong background in statistics, programming (often Python or R), and data analysis, usually supported by a degree in a quantitative field. Familiarity with machine learning libraries (like scikit-learn or TensorFlow), big data tools (such as Hadoop or Spark), and data visualization platforms is typically required. Critical thinking, problem-solving, and effective communication are vital soft skills for translating complex data insights into actionable business strategies. These skills and qualities are essential for extracting value from data, driving informed decisions, and effectively collaborating with multidisciplinary teams.

Is 40 too late for data science?

Data science is a field open to individuals of all ages, and many professionals transition into it later in their careers. Success often depends on acquiring relevant skills such as programming, statistics, and machine learning, which can be learned through online courses, bootcamps, or degrees regardless of age.

What are some common challenges faced by data scientists when working with real-world datasets?

Data scientists often encounter challenges such as missing or inconsistent data, unstructured formats, and noisy information in real-world datasets. Cleaning and preprocessing data to ensure its quality can be time-consuming but is critical for building accurate models. Additionally, data scientists may work closely with domain experts and other team members to better understand the data's context and ensure their analyses align with business objectives. Overcoming these challenges requires strong problem-solving skills and effective collaboration within cross-functional teams.

What is data science?

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It combines skills from statistics, computer science, and domain expertise to analyze and interpret complex data sets. Data scientists work with large amounts of data to identify patterns, make predictions, and help organizations make data-driven decisions.

What jobs can a Data Scientist do?

A Data Scientist can work in roles such as data analyst, machine learning engineer, data engineer, or business intelligence analyst. These roles involve analyzing large datasets, developing predictive models, and using tools like Python, R, and SQL to support decision-making across various industries.

What is the difference between Data Science vs Data Analyst?

AspectData ScienceData Analyst
Required skillsStatistics, programming (Python, R), machine learningData visualization, SQL, basic statistics
Work environmentDeveloping models, predictive analytics, researchReporting, data cleaning, descriptive analysis
Tools usedPython, R, Jupyter, TensorFlowExcel, SQL, Tableau, Power BI
Industry usageTech, finance, healthcare, e-commerceRetail, marketing, finance, healthcare

Data Science and Data Analyst roles often overlap but differ mainly in scope. Data Scientists focus on building predictive models and advanced analytics, requiring programming and machine learning skills. Data Analysts primarily handle data cleaning, reporting, and visualization. Both roles are essential in data-driven industries, but Data Science is more technical and research-oriented, while Data Analysis emphasizes interpreting data for business insights.

What work do you do as a Data Scientist?

A Data Scientist analyzes large datasets to extract insights, build predictive models, and inform business decisions. They use programming languages like Python or R, and tools such as SQL and machine learning frameworks, often working in collaborative environments with data engineers and analysts.

What Does a Data Scientist Do?

As a Data Scientist, you are qualified to work in such diverse fields as research and development, politics, advertising and marketing, technology, healthcare, government, and higher education as well as multiple others. In general, your duties and responsibilities will be to compile and analyze relevant statistics and turn those numbers into algorithms that reveal insights that can be used by other researchers in their areas of study. Data Science can reveal things like consumer buying habits or the likelihood of success for a course of action. Other duties might vary, depending on your unique field of specialty. Related areas in which a Data Scientist might wish to focus include work as a Data Analyst, Machine Learning Engineer, and Project Manager.
What are the most commonly searched types of Data Science jobs in Ontario? The most popular types of Data Science jobs in Ontario are:
What are popular job titles related to Data Science jobs in Ontario? For Data Science jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Data Science jobs in Ontario look for? The top searched job categories for Data Science jobs in Ontario are:
What cities in Ontario are hiring for Data Science jobs? Cities in Ontario with the most Data Science job openings:
Director, Data Science

Full-time

Posted 15 days ago


Job description

Our Purpose

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we're helping build asustainableeconomy where everyone can prosper. We support a wide range of digital payments choices, making transactionssecure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.

Title and Summary

Director, Data ScienceOverview
The Security Solutions Data Science team is responsible for delivering Artificial Intelligence (AI) and Machine Learning (ML) models that support Mastercard's Identity and risk products across the payment networks. These models are designed to be production-ready and to power high-value capabilities that protect digital transactions and enable trusted decisioning at scale.
Beyond model development, the organization is responsible for building scalable, repeatable, and resilient data science capabilities that cover the end-to-end lifecycle of machine learning solutions, from data acquisition and feature engineering through experimentation, validation, deployment, and monitoring. These systems must not only perform effectively in production, but also be built in a way that is industrialized, maintainable, and aligned with broader business and platform needs.
Services within Mastercard is responsible for acquiring, engaging, and retaining customers by managing fraud and risk, enhancing cybersecurity, and improving the digital payments experience. Within this mission, the Identity Data Science portfolio plays a significant role in developing intelligence-driven solutions that improve how risk is understood and managed across the merchant lifecycle.
We are looking for a highly skilled and strategic Director to lead a Data Science team that focuses on solving merchant risk during onboarding and monitoring. This role is critical in driving the research and development of a merchant registry and profiling capability within the Identity Data Science portfolio. This includes setting the strategy for how merchant intelligence is built and scaled, guiding the development of reusable data science assets and profiling frameworks, and ensuring strong execution across product, engineering, and data science partners. You will help shape both the technical direction and operational model needed to turn this capability into a durable and scalable advantage.
Role
Key responsibilities include:
Define and execute the strategy for solving merchant risk during onboarding and monitoring through the research and development of a merchant registry and profiling capability
Lead, coach, and develop high-performing teams of Data Scientists, including hiring, mentorship, and performance management
Build a strong team culture focused on collaboration, accountability, and continuous learning
Guide the design of scalable machine learning and analytical systems using modern data and cloud platforms such as Databricks and Sparks.
Establish best practices for problem framing, technique selection, experimentation, and validation across the team
Ensure teams identify appropriate approaches for business problems and rigorously validate solutions against both technical and business outcomes
Partner closely with Product, Engineering, and other stakeholders to translate strategic needs into data science roadmaps and deliverables
Drive Agile delivery practices that support iterative development, measurable outcomes, and continuous improvement
Promote reusable data assets, standardization, and scalable workflows that strengthen long-term execution
Communicate strategy, progress, trade-offs, and business value clearly to senior leadership
Balance long-term vision with near-term delivery to maximize impact and time-to-value
All About You
Essential Skills to be successful:
Advanced degree (Master's or PhD preferred) in Data Science, ML, or related field
Extensive experience leading a Data Science team and drive innovation with inspiration.
Experienced being a great people leader but able to dig into the work where necessary
A proven track record of deploying high performance machine learning models at scale in a production environment
Strong proficiency with Python, SQL, along with experience using scalable Machine Learning and Cloud frameworks
Strong ability to guide teams in identifying appropriate techniques and validating solutions rigorously
Critical thinking and a drive to produce high-quality work, ensuring that all solutions meet rigorous standards
Demonstrated success translating complex business problems into strategic data science initiatives. Ability to lead through ambiguity and change
Strong understanding of Agile methodologies, with the ability to drive iterative delivery across cross-functional teams
Excellent stakeholder management, communication to both technical and non-technical audiences, and leadership skills
Proven ability to build teams, influence roadmaps, and deliver measurable business value in complex environments
High-energy and self-driven orientation
Nice to Have
Experience building or scaling shared data science platforms, registries, or enterprise data assets
Familiarity with merchant risk, fraud, or identity ecosystems
Experience driving cross-team standardization and reusable capabilities
Exposure to governance frameworks for model validation and AI systemsMastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Corporate Security Responsibility


All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:

  • Abide by Mastercard's security policies and practices;

  • Ensure the confidentiality and integrity of the information being accessed;

  • Report any suspected information security violation or breach, and

  • Complete all periodic mandatory security trainings in accordance with Mastercard's guidelines.

In line with Mastercard's total compensation philosophy and assuming that the job will be performed in Canada, the successful candidate will be offered a competitive pay based on location, experience and other qualifications for the role and may be eligible to participate in a discretionary annual incentive program. This is a pipeline posting for future opportunities within our team.

Pay Ranges

Vancouver, Canada: $154,000 - $247,000 CADToronto, Canada: $154,000 - $247,000 CAD