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Graduate 2025 Scientist I (Driver Pricing) - Job Opportunity at Uber

Toronto, Canada
Full-time
Entry-level
Posted: July 3, 2025
On-site
CAD 114,000-120,000 per year

Benefits

Competitive bonus program participation providing performance-based financial rewards above base compensation
Equity award opportunities offering ownership stake in one of the world's leading mobility platforms
Comprehensive benefits package including health, dental, and wellness programs typical of top-tier tech companies
Professional development opportunities through exposure to cutting-edge data science methodologies and large-scale marketplace optimization
Access to Uber's global network and cross-functional collaboration with world-class product managers and engineers

Key Responsibilities

Drive strategic marketplace optimization by building and validating statistical and machine learning models that directly impact millions of driver transactions globally
Lead data-driven business intelligence initiatives through comprehensive exploratory analysis that identifies market trends and informs executive decision-making
Architect and execute A/B testing frameworks to measure product impact, directly influencing multi-million dollar pricing strategy decisions
Engineer robust data pipelines by preparing and cleaning large-scale datasets that power Uber's core pricing algorithms
Collaborate cross-functionally with senior scientists, product managers, and engineers to implement solutions that shape the future of mobility marketplace dynamics
Continuously innovate by learning and applying advanced methodologies in machine learning, causal inference, and experimentation to solve complex marketplace challenges

Requirements

Education

Bachelor's or Master's degree in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields

Experience

0-2 years of relevant industry or internship experience

Required Skills

Foundational knowledge of Python or R for data analysis Familiarity with SQL A solid understanding of fundamental concepts in statistics and machine learning Experience with data visualization tools A strong desire to learn, ask questions, and solve problems Good communication skills and the ability to work effectively in a team environment Exposure to A/B testing or experimental design concepts
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Sauge AI Market Intelligence

Industry Trends

The mobility and ride-sharing industry is experiencing unprecedented growth in algorithmic pricing optimization, with companies increasingly relying on sophisticated machine learning models to balance supply and demand in real-time marketplaces. This trend is driven by the need to optimize driver earnings while maintaining competitive pricing for consumers, creating a complex multi-sided marketplace challenge that requires advanced data science capabilities. There is a significant shift toward causal inference methodologies in pricing strategy, moving beyond traditional correlation-based approaches to understand true cause-and-effect relationships in marketplace dynamics. This evolution reflects the maturation of the industry and the need for more sophisticated analytical approaches to drive sustainable growth. The integration of experimentation frameworks and A/B testing has become critical for product development in the mobility sector, with companies investing heavily in controlled experimentation capabilities to validate pricing changes before full-scale deployment. This approach minimizes risk while maximizing learning opportunities in highly competitive markets.

Salary Evaluation

The offered salary range of CAD 114,000-120,000 is highly competitive for entry-level data science positions in the Toronto market, representing approximately 15-20% premium above typical graduate starting salaries. When combined with equity and bonus opportunities, the total compensation package likely exceeds CAD 140,000-160,000 annually, positioning this role among the top-tier opportunities for new graduates in the Canadian tech sector.

Role Significance

The Driver Pricing team likely consists of 8-12 data scientists across various seniority levels, with 2-3 senior scientists providing guidance to 4-6 junior and mid-level scientists. This structure ensures strong mentorship opportunities while maintaining the agility needed for rapid experimentation and model deployment.
This entry-level position offers exceptional exposure to strategic decision-making processes typically reserved for more senior roles, given the critical nature of pricing systems to Uber's core business model. The role provides direct influence on marketplace performance while operating under senior scientist mentorship, creating an accelerated learning environment with high business impact.

Key Projects

Development and optimization of dynamic pricing algorithms that adjust driver compensation based on real-time supply and demand conditions across multiple geographic markets Implementation of sophisticated A/B testing frameworks to evaluate the impact of pricing changes on driver retention, trip acceptance rates, and overall marketplace health Construction of causal inference models to understand the true impact of pricing interventions on driver behavior and long-term marketplace sustainability Creation of predictive models that forecast driver supply patterns and optimize pricing strategies to maintain marketplace balance during peak and off-peak periods

Success Factors

Developing strong analytical thinking capabilities combined with business acumen to understand how technical solutions directly impact marketplace dynamics and driver experience Building proficiency in advanced statistical methods and machine learning techniques while maintaining focus on practical business applications and measurable outcomes Cultivating effective communication skills to translate complex analytical findings into actionable insights for cross-functional stakeholders including product managers and engineering teams Demonstrating adaptability and continuous learning mindset given the rapidly evolving nature of marketplace optimization and the introduction of new methodologies in data science

Market Demand

Extremely high demand exists for data scientists specializing in marketplace optimization and pricing strategy, particularly those with experience in experimentation and causal inference. The mobility sector's continued expansion and the critical nature of pricing algorithms make these roles essential for competitive advantage.

Important Skills

Critical Skills

Python and R programming skills are absolutely essential as they form the foundation for all analytical work, model development, and data manipulation tasks. Proficiency in these languages directly correlates with productivity and the ability to contribute meaningfully to complex pricing algorithm development. SQL expertise is crucial for data extraction and manipulation given the massive scale of Uber's data infrastructure. Advanced SQL skills enable efficient data processing and are prerequisite for conducting meaningful analysis on transaction and marketplace data. Statistical and machine learning fundamentals are core to understanding model performance, experimental design, and the theoretical foundations underlying pricing algorithms. This knowledge is essential for making informed decisions about model selection and interpretation of results.

Beneficial Skills

Experience with distributed computing frameworks like Spark or Hadoop would be valuable given the scale of data processing required for marketplace optimization Knowledge of causal inference methodologies and experimental design principles would accelerate contribution to A/B testing and impact measurement initiatives Understanding of economic principles and marketplace dynamics would enhance ability to develop pricing strategies that account for complex supply and demand interactions Familiarity with machine learning operations (MLOps) practices would be beneficial for understanding how models are deployed and monitored in production environments

Unique Aspects

Direct exposure to one of the world's most sophisticated pricing algorithms affecting millions of daily transactions, providing unparalleled learning opportunities in real-time marketplace optimization
Opportunity to work with massive-scale datasets and cutting-edge experimentation frameworks that are typically only available at top-tier technology companies
Integration of multiple disciplines including machine learning, economics, and operations research to solve complex marketplace challenges that have direct business impact
Access to Uber's global network of data scientists and engineers, providing exposure to diverse perspectives and advanced methodologies from around the world

Career Growth

Typical progression to mid-level roles (Scientist II) occurs within 18-24 months given the accelerated learning environment and high-impact nature of the work. Senior-level positions are generally achievable within 3-4 years with strong performance and demonstrated leadership capabilities.

Potential Next Roles

Scientist II or Senior Data Scientist positions focusing on advanced marketplace optimization and pricing strategy development Product Data Science roles with expanded responsibility for driving product strategy and cross-functional collaboration Machine Learning Engineer positions specializing in the deployment and scaling of pricing algorithms in production environments Data Science Manager roles overseeing teams responsible for marketplace optimization and pricing strategy across multiple business units

Company Overview

Uber

Uber represents the global leader in mobility technology, operating in over 70 countries with a platform that facilitates billions of trips annually. The company has evolved from a ride-sharing startup to a comprehensive mobility ecosystem encompassing ride-sharing, food delivery, freight, and autonomous vehicle development. Uber's data science organization is recognized as one of the most sophisticated in the industry, with particular expertise in marketplace optimization and large-scale experimentation.

Uber maintains a dominant position in the global mobility market with significant competitive advantages in data science capabilities, marketplace optimization, and algorithmic pricing. The company's scale provides unique opportunities to work with datasets and challenges that exist nowhere else in the industry, making it an exceptional training ground for data scientists specializing in marketplace dynamics.
Toronto represents a strategic hub for Uber's North American operations, with significant investment in local talent and technology development. The city's strong tech ecosystem and proximity to top universities make it an ideal location for advanced data science work, while Canada's immigration policies support long-term career development for international professionals.
Uber's culture emphasizes data-driven decision making, rapid experimentation, and cross-functional collaboration. The company's "Let Builders Build" philosophy encourages innovation and ownership, while the scale of operations provides opportunities to see the direct impact of analytical work on millions of users globally. The environment is fast-paced and results-oriented, with strong emphasis on learning and professional development.
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