Data Scientist - Inference, Safety and Customer Care

United States Digital Space LLC; Toronto; On-site; CAD 108,000 - 135,000; Full time

Benefits offered by this job

Extended health and dental coverage

Life insurance and disability benefits

Mental health benefits

Family building benefits

Child care and pet benefits

Health Care Savings Account

RRSP with company match

Flexible paid time off

Paid parental leave (18 weeks)

Job summary

United States Digital Space LLC is seeking a Data Scientist for Causal Inference in SCC, collaborating with engineers, product managers, designers, and operations to deliver personalized experiences and trustworthy decisions for riders and drivers.

You will apply rigorous causal inference to AI-powered support products, differentiate service, and optimize operations, with a bias toward high-impact results.

Qualifications

• 2+ years of causal inference or data science with a quantitative degree or PhD
• Strong knowledge of causal inference and experimental design
• Experience with uplift modeling / CATE estimation
• Statistics applied to unstructured problems with measurable results
• Expertise in SQL and large-scale data platforms
• Python proficiency in production coding environments
• Clear, effective communication to diverse audiences
• Experience with AI/LLM applications is a nice-to-have

Responsibilities

• Inference & measurement: design causal inference frameworks and models to measure interventions
• Modeling: build and deploy causal ML models across the lifecycle
• Optimization: analyze tradeoffs among accuracy, coverage, UX, and cost
• Collaborate cross-functionally with Product, Design, Engineering, Operations, Analytics
• Influence decisions by communicating learnings to leadership
• Empowerment: help scale data science capabilities within SCC

Skills

Causal inference

Experimental design

Uplift modeling

SQL

Python

Communication

Project management

Collaboration

AI/LLM experience

Education

Master's degree in a quantitative field

PhD in a relevant field

Tools

SQL

Python

Job description

At the company, 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 the company manages over 1.7 million monthly human and AI interactions and serves as the company'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 the company, 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 the company 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.

Responsibilities:

• 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.

Experience:

• 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.

Benefits:

• 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 the company 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 the company'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
• the company 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 the company ride credits

the company is committed to creating an inclusive workforce that fosters belonging. the company 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. the company 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.

the company 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. the company 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 the company 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.

the company may use artificial intelligence to screen applicants, however, the company employees make the ultimate selection and hiring decisions.

This job fills an existing vacancy.