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Causal Inference Jobs in Ontario (NOW HIRING)

Experience designing, running, and analyzing complex experiments or leveraging causal inference designs * A builder's mindset with a willingness to question assumptions and conventional wisdom

Develop causal inference methodologies to understand true incrementality of product changes. * Ensure models are observable, explainable where needed, and continuously improved post-launch Product ...

Develop causal inference methodologies to understand true incrementality of product changes. * Ensure models are observable, explainable where needed, and continuously improved post-launch Product ...

Personally build and deploy sophisticated statistical models including MMM, causal inference models, time series forecasting, and experimental design frameworks * Lead end-to-end model development ...

The Data Scientist provides deep technical leadership in modern ML methods, including time-series forecasting, optimization, simulation, causal inference, and LLM/NLP whereappropriate. In addition ...

The Data Scientist provides deep technical leadership in modern ML methods, including time-series forecasting, optimization, simulation, causal inference, and LLM/NLP whereappropriate. In addition ...

Senior Data Analyst (m/f/d)

Toronto, ON · On-site +1

CA$110K - CA$140K/yr

Strong knowledge of statistics, experimentation, causal inference, and KPI design. * Experience partnering with Data Science and ML teams. * Hands-on experience with AI tools for analytics ...

Strong knowledge of time-series forecasting, causal inference, or incrementality measurement. * Relevant Google Cloud certifications such as Professional Machine Learning Engineer or Professional ...

Senior Data Analyst

Toronto, ON · Hybrid

CA$105K - CA$115K/yr

Hands-on experience designing and analyzing experiments (A/B testing, causal inference, statistical interpretation) * Proven ability to independently structure ambiguous problems and deliver ...

Data Scientist II

Toronto, ON · On-site

CA$81K - CA$115K/yr

Hands-on experience with machine learning techniques, including supervised and unsupervised learning, reinforcement learning, and causal inference. * Experience building, training, and deploying ...

Continuously monitor customer experience performance using advanced analytics segmentation, cohort analysis, and causal inference to identify gaps, improvement areas, and build improvement plans with ...

Staff Data Scientist

Toronto, ON · Hybrid

CA$192K - CA$230K/yr

Tackle high-ambiguity challenges, such as integrating causal inference or deep learning into global forecasts. GenAI & LLM Innovation: * Act as the subject matter expert for Generative AI; design RAG ...

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Causal Inference information

See Ontario salary details

$21K

$114.4K

$169.5K

How much do causal inference jobs pay per year?

As of Jul 13, 2026, the average yearly pay for causal inference in Ontario is $114,369.00, according to ZipRecruiter salary data. Most workers in this role earn between $57,500.00 and $156,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Causal Inference position, and why are they important?

Success in a Causal Inference role requires strong statistical knowledge, expertise in experimental and quasi-experimental methodologies, and advanced proficiency in programming languages like R or Python, typically acquired with an advanced degree in statistics, economics, data science, or a related field. Familiarity with specialized statistical software (such as Stata, SAS, or causal inference packages in R/Python), as well as experience with large datasets and machine learning tools, is highly valued. Excellent problem-solving abilities, clear communication, and collaboration skills are essential soft skills for effectively conveying complex findings to diverse teams. These competencies are critical to producing reliable insights that guide evidence-based decision-making in business, healthcare, or policy settings.

What are some common challenges faced in a Causal Inference position?

Professionals in Causal Inference often encounter challenges such as dealing with confounding factors, addressing selection bias, and ensuring the validity of assumptions behind statistical models. They must carefully design experiments or leverage observational data while staying vigilant about potential data quality issues and model limitations. Collaboration with subject matter experts, data engineers, and business stakeholders is common to ensure accurate contextualization of results. Overcoming these challenges requires a mix of technical acumen and strong communication skills to translate complex analyses into actionable recommendations.

What is a Causal Inference job?

A Causal Inference job involves using statistical and computational methods to determine cause-and-effect relationships from data. Professionals in this field work with observational and experimental data to identify causal impacts, often in domains like economics, healthcare, social sciences, and technology. They apply techniques such as propensity score matching, instrumental variables, and difference-in-differences to ensure rigorous analysis. These roles are commonly found in academia, policy research, and data science teams within tech and finance companies. Strong skills in statistics, programming (e.g., Python, R), and experimental design are typically required.

What are popular job titles related to Causal Inference jobs in Ontario? For Causal Inference jobs in Ontario, the most frequently searched job titles are:
What job categories do people searching Causal Inference jobs in Ontario look for? The top searched job categories for Causal Inference jobs in Ontario are:
Data Scientist

Data Scientist

Stripe

Toronto, ON • On-site

Other

Re-posted 17 days ago


Job description

Who we areAbout Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies-from the world's largest enterprises to the most ambitious startups-use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.

About the team

Our Data Science team partners deeply with teams across Stripe to ensure that our users, our products, and our business have the models, data products, and insights needed to make decisions and grow responsibly. We're looking for data scientists with a passion for analyzing data, building machine learning and statistical models, and running experiments to drive impact. Our work is broad and varied, influencing how our products work (e.g., understanding user needs, preventing fraud, or optimizing charge flows), how our business works (forecasting key outcomes, managing liquidity, and quantifying risk exposure), how our go-to-market motions operate (designing growth experiments, optimizing marketing investments, refining sales processes, and estimating causal effects), and everything in between. We have a variety of Data Science roles and teams across Stripe and will seek to align you to the most relevant team based on your background.

What you'll do

We're looking for a variety of Data Scientists to partner with the Product, Finance, Payments, Security, Risk, Growth, and Go-to-Market teams. You'll work closely with a specific part of the business, playing a crucial role in optimizing our systems and leveraging data to make strategic business decisions. As Data Scientists at Stripe, it's our mission to ensure that the company strategy, products, and user interactions make smart use of our rich data, using techniques like machine learning, statistical modeling, causal inference, optimization, experimentation, and all forms of analytics.

Who you are

We're looking for someone who meets the minimum requirements to be considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.

Minimum requirements
  • PhD with 3 years, MS or MA with 6 years, or BS or BA with 8 years of data science or quantitative modeling experience
  • Proficiency in SQL and a computing language such as Python or R
  • Experience in working with cross-functional teams to deliver results
  • Ability to communicate results clearly and a focus on driving impact
  • A demonstrated ability to manage and deliver on multiple projects with a high attention to detail
  • Strong business acumen and experience in synthesizing complex analyses into actionable recommendations
  • Proficiency with AI tools to accelerate model development, analysis, and coding
Preferred qualifications
  • Strong knowledge and hands-on experience in several of the following areas: machine learning, statistics, optimization, product analytics, causal inference, and experimentation
  • Experience deploying models in production and adjusting model thresholds to improve performance
  • Experience designing, running, and analyzing complex experiments or leveraging causal inference designs
  • A builder's mindset with a willingness to question assumptions and conventional wisdom
  • Experience with distributed tools such as Spark, Hadoop, etc.
  • A PhD or MS in a quantitative field (e.g., Statistics, Engineering, Mathematics, Economics, Quantitative Finance, Sciences, Operations Research)