As a Data Scientist working on Causal Inference in SCC, you'll partner with a strong team of engineers, product managers, designers, and operations leaders to deliver a personalized and exceptional ...
As a Data Scientist working on Causal Inference in SCC, you'll partner with a strong team of engineers, product managers, designers, and operations leaders to deliver a personalized and exceptional ...
Advance Experimentation and Causal Inference at Scale: Lead the evolution of Reddit's experimentation and lift methodologies across Brand Lift, Conversion Lift, Split Testing, and emerging ...
Advance Experimentation and Causal Inference at Scale: Lead the evolution of Reddit's experimentation and lift methodologies across Brand Lift, Conversion Lift, Split Testing, and emerging ...
Data Scientist, Link
Toronto, ON · On-site
Experience designing, running, and analyzing complex experiments or leveraging causal inference designs * Proficiency in SQL and Python * Experience in working with cross-functional teams to deliver ...
Data Scientist, Link
Toronto, ON · On-site
Experience designing, running, and analyzing complex experiments or leveraging causal inference designs * Proficiency in SQL and Python * Experience in working with cross-functional teams to deliver ...
Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk ...
Data scientists on this team apply supervised and unsupervised machine learning, statistical modeling, causal inference, optimization, and experimentation to some of the most consequential risk ...
You'll work with massive, high-dimensional data sets , applying advanced statistical methods, machine learning, and causal inference to answer questions that directly influence product vision and ...
You'll work with massive, high-dimensional data sets , applying advanced statistical methods, machine learning, and causal inference to answer questions that directly influence product vision and ...
Staff Data Scientist
Toronto, ON · On-site
Mentor Senior and mid-level Data Scientists while raising technical rigor across statistical thinking,causal inference, optimization, and experimentation * Shape the evolution of reusable DS ...
Staff Data Scientist
Toronto, ON · On-site
Mentor Senior and mid-level Data Scientists while raising technical rigor across statistical thinking,causal inference, optimization, and experimentation * Shape the evolution of reusable DS ...
Deep expertise in machine learning, statistics, and data science with experience in optimization, A/B testing, and causal inference * Deep expertise in production ML infrastructure including model ...
Deep expertise in machine learning, statistics, and data science with experience in optimization, A/B testing, and causal inference * Deep expertise in production ML infrastructure including model ...
Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies. Experience: * Advanced degree (MS or PhD, PhD preferred) in a ...
Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies. Experience: * Advanced degree (MS or PhD, PhD preferred) in a ...
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 ...
Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies * Mentor and guide the professional and technical development of your ...
Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies * Mentor and guide the professional and technical development of your ...
Director, Data Science/ML
Toronto, ON · On-site
Develop causal inference methodologies to understand true incrementality of product changes. * Ensure models are observable, explainable where needed, and continuously improved post-launch Product ...
Director, Data Science/ML
Toronto, ON · On-site
Develop causal inference methodologies to understand true incrementality of product changes. * Ensure models are observable, explainable where needed, and continuously improved post-launch Product ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Use state-of-the-art scientific technologies including Generative AI, Classical Machine Learning, Causal Inference, Natural Language Processing, and Computer Vision to develop state of the art models ...
Senior Data Scientist
Toronto, ON · On-site
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 ...
Senior Data Scientist
Toronto, ON · On-site
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 ...
Data Scientist, Causal Inference - New Product Development * Data Scientist Lead, AI and Data - Elevate Program 2025 * Machine Learning Developer Intern (4 month term - Toronto or Montreal) #J-18808 ...
Data Scientist, Causal Inference - New Product Development * Data Scientist Lead, AI and Data - Elevate Program 2025 * Machine Learning Developer Intern (4 month term - Toronto or Montreal) #J-18808 ...
Data Scientist II
Toronto, ON · On-site
CA$81K - CA$115K/yr
Applying marketing analytics techniques MTA (Multi-touch attribution), MMM (Media Mix Model) and Causal inference method to understand the true value of marketing and suggest optimization ...
Data Scientist II
Toronto, ON · On-site
CA$81K - CA$115K/yr
Applying marketing analytics techniques MTA (Multi-touch attribution), MMM (Media Mix Model) and Causal inference method to understand the true value of marketing and suggest optimization ...
Bring rigor (power analysis, causal inference, guardrail metrics) to a business moving fast. * Build the reporting backbone. Own the core dashboards and self-serve tooling the segment runs on ...
Bring rigor (power analysis, causal inference, guardrail metrics) to a business moving fast. * Build the reporting backbone. Own the core dashboards and self-serve tooling the segment runs on ...
Bring rigor (power analysis, causal inference, guardrail metrics) to a business moving fast. * Build the reporting backbone. Own the core dashboards and self-serve tooling the segment runs on ...
Bring rigor (power analysis, causal inference, guardrail metrics) to a business moving fast. * Build the reporting backbone. Own the core dashboards and self-serve tooling the segment runs on ...
Senior Applied AI/ML Scientist - Retailer Growth
Kitchener, ON · On-site
CA$180K - CA$247K/yr
Implement rigorous experimentation and causal inference frameworks to quantify the impact of growth levers. * Engineer scalable solutions for complex challenges inherent to two-sided marketplace ...
Senior Applied AI/ML Scientist - Retailer Growth
Kitchener, ON · On-site
CA$180K - CA$247K/yr
Implement rigorous experimentation and causal inference frameworks to quantify the impact of growth levers. * Engineer scalable solutions for complex challenges inherent to two-sided marketplace ...
You will work with colleagues with deep expertise in ML, DL, NLP, Gen AI, and Causal Inference with a diverse range of backgrounds We partner closely with top-notch engineers, product managers, sales ...
You will work with colleagues with deep expertise in ML, DL, NLP, Gen AI, and Causal Inference with a diverse range of backgrounds We partner closely with top-notch engineers, product managers, sales ...
Senior/Lead Data Scientist
Mississauga, ON · On-site +1
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/Lead Data Scientist
Mississauga, ON · On-site +1
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 ...
Causal Inference information
See Ontario salary details
$21K - $34.5K
2% of jobs
$34.5K - $48K
7% of jobs
$58.8K is the 25th percentile. Wages below this are outliers.
$48K - $61.5K
20% of jobs
$61.5K - $75K
7% of jobs
$75K - $88.5K
2% of jobs
$88.5K - $102K
0% of jobs
$102K - $115.5K
0% of jobs
$115.5K - $129K
0% of jobs
The median wage is $141.9K / yr.
$129K - $142.5K
12% of jobs
$155.9K is the 75th percentile. Wages above this are outliers.
$142.5K - $156K
25% of jobs
$156K - $169.5K
25% of jobs
$21K
$114.4K
$169.5K
How much do causal inference jobs pay per year?
As of Sep 2, 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 is a causal inference?
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 skills and qualifications are needed for a causal inference position?
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 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 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:
Full-time
Medical, Dental, Life, Retirement, PTO
Posted 28 days ago
Lyft rating
7.6
Based on 33 frontline employees who took The Breakroom Quiz
2nd of 9 rated taxi private hire
Job description
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Safety and Customer Care (SCC) team at Lyft manages over 1.7 million monthly human and AI interactions and serves as Lyft's primary direct touchpoint with riders and drivers. We handle critical infrastructure that powers both human associates and AI agents to make riders and drivers feel safe and comfortable while riding or driving with Lyft, transforming every support interaction into a moment of genuine connection.
As a Data Scientist working on Causal Inference in SCC, you'll partner with a strong team of engineers, product managers, designers, and operations leaders to deliver a personalized and exceptional experience for Lyft customers, using rigorous causal inference to guide the highest-stakes decisions we make.
We're looking for a motivated and talented Data Scientist with deep causal inference expertise to join the SCC Data Science team. You'll partner closely with the area's tech lead on high-impact work spanning AI-powered support products, differentiated service, and operations optimization.
The ideal candidate brings sharp applied inference intuition, a bias toward impact, and the ability to cut through ambiguity in complex problem spaces. You'll work on projects like:
- Design rigorous experiments and quasi-experiments to measure the causal impact of SCC product and AI-agent launches, and drive data-informed launch decisions.
- Build causal ML models to optimize concession budget allocation, targeting the right support credit, to the right rider or driver, at the right moment to maximize trust and business impact.
- Quantify the long-term effects of support-experience changes on rider and driver retention, and uncover heterogeneous treatment effects across our community.
- Deliver strategic insights on quality-cost tradeoffs, empowering leadership to balance service quality, coverage, and operational cost as we scale AI-powered support.
- Inference & Measurement: Design and implement causal inference frameworks and statistical models to measure the impact of interventions, evaluate system performance, and surface opportunities for improvement.
- Modeling: Build, evaluate, and iterate on causal ML models that power high-stakes decisions, applying best practices across the full model lifecycle, from feature engineering to production deployment.
- Optimization: Develop frameworks to analyze tradeoffs between competing objectives (accuracy, coverage, user experience, and operational cost), and propose strategies to improve overall effectiveness.
- Collaborate Cross-Functionally: Build strong relationships with partners across Product, Design, Engineering, Operations, and Analytics to drive collaboration and innovation.
- Influence Decisions: Communicate learnings to leaders and stakeholders in a clear, compelling way that drives informed, data-driven decision-making.
- Empowerment: Think strategically about how to scale and evolve data science capabilities within SCC, contributing to the long-term vision for how science drives platform outcomes.
- 2+ years of industry experience in causal inference or data science with a Master's degree in a quantitative field (statistics, economics, computer science, etc.), or a PhD in a relevant field.
- Strong knowledge of causal inference and experimental design.
- Experience with uplift modeling / heterogeneous treatment effect (CATE) estimation.
- Proven ability to apply statistics to unstructured problems and deliver measurable results.
- Expertise in SQL and experience with large-scale data platforms.
- Proficiency in Python and working within production coding environments.
- Proven ability to communicate clearly and effectively to audiences of varying technical levels.
- Excellent project management, communication, and collaboration skills.
- Experience partnering with operational teams and support systems (customer care workflows, agent operations, or credit budget allocation)
- Experience working with AI/LLM applications (LLM-powered agents, retrieval systems, or evaluation frameworks) is nice to have.
- Extended health and dental coverage options, along with life insurance and disability benefits
- Mental health benefits
- Family building benefits
- Child care and pet benefits
- Access to a Lyft funded Health Care Savings Account
- RRSP plan with company match to help save for your future
- In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service
- Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
- Subsidized commuter benefits and Lyft ride credits
Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.
Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule - Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid
The expected base pay range for this position in the Toronto area is CAD $108,000 - CAD $135,000, not inclusive of potential equity offering, bonus or benefits. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
Lyft may use artificial intelligence to screen applicants, however, Lyft employees make the ultimate selection and hiring decisions.
This job fills an existing vacancy.
About Lyft
Sourced by ZipRecruiter
At Lyft, our mission is to improve people's lives with the world's best transportation. To do this, we start with our own community by creating an open, inclusive, and diverse organization.
Industry
Ground public transportation
Company size
5,001 - 10,000 Employees
Headquarters location
San Francisco, CA, US
Year founded
2012