Data Scientist, Optimization - Real-Time Supply Management

United States Digital Space LLC; Toronto; Hybrid; 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

RRSP plan with company match

Subsidized commuter benefits

Hybrid work schedule with in-office 3+

Flexible paid time off

Job summary

United States Digital Space LLC in Toronto seeks a Data Scientist specializing in Algorithms to design and scale incentive systems for drivers. You will collaborate with product and engineering to deploy optimization models and experiment-driven decision-making.

Ideal candidates hold advanced degrees and have 2+ years in optimization, causal inference, or ML, with strong Python and data-analysis skills and a hands-on, entrepreneurial mindset.

Qualifications

• Advanced degree (MS or PhD) in a quantitative field; or equivalent work experience.
• 2+ years hands-on experience in optimization, causal inference, or machine learning.
• End-to-end data experience: querying, aggregation, analysis, and visualization.
• Proficiency with Python.
• Strong ability to collaborate and communicate in a team setting.

Responsibilities

• Collaborate with engineering and product to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms.
• Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting.
• Write production model code; collaborate with Software Engineers to implement algorithms in production.
• Perform exploratory data analysis to gain a deeper understanding of the marketplace and its users.
• Communicate findings and facilitate launch decisions with technical and non-technical stakeholders.
• Ensure robust experimentation and causal inference methodologies are applied to measure the impact of new features and strategies.

Skills

Python

Optimization

Causal Inference

Machine Learning

A/B Testing

Data Analysis

Education

MS or PhD in quantitative field

Tools

SQL

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.

Data Science is at the heart of the company’s products and decision-making. Data Scientists at the company operate in dynamic environments, moving quickly to build the world’s best transportation solutions. We tackle a wide range of challenges - from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Driver Incentives Science owns the algorithms and systems behind incentive design, influencing driver engagement and marketplace efficiency — from real-time supply positioning to longer-horizon earnings and engagement programs. The team is responsible for designing pay and incentive mechanisms that are efficient and good for driver experience over the long run.

As a Data Scientist specializing in Algorithms, you'll partner closely with product, engineering, and operations leaders to build and scale incentive systems, shape long-term mechanism design strategy, and deliver on critical business goals tied to marketplace efficiency and driver earnings. Candidates with strong optimization backgrounds — think mathematical programming, control theory, or operations research — are a great fit, though we welcome strong candidates from machine learning or causal inference as well. The ideal candidate thrives in a fast-paced environment and brings a hands‑on, entrepreneurial mindset to drive results.

Responsibilities:

• Collaborate with engineering and product teams to design, implement, and iterate on new features and algorithmic improvements for driver incentives and pay mechanisms.

• Design, develop, and deploy optimization models, algorithms, and systems for problems such as budget allocation, multidimensional cost-curve development, and incentive targeting.

• Write production model code; collaborate with Software Engineers to implement algorithms in production.

• Perform exploratory data analysis to gain a deeper understanding of the marketplace and its users.

• Communicate findings and facilitate launch decisions with technical and non-technical stakeholders.

• 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 quantitative field like Operations Research, Applied Math, Computer Science, Statistics, Engineering, or a related area; or equivalent work experience.

• Passion for solving unstructured and non-standard mathematical problems, with 2+ years of hands‑on experience in optimization (preferred), causal inference, or machine learning.

• End-to-end experience with data, including querying, aggregation, analysis, and visualization.

• Proficiency with Python.

• Strong ability to collaborate and communicate with others in a team setting.

• Experience seeking out and adopting new methods and techniques.

• Experience designing, running, and analyzing A/B tests to validate hypotheses and inform decision-making.

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.