Lead Data Scientist at Upwork Global Inc

Toronto, Ontario, Canada -

Full Time

Start Date

Immediate

Expiry Date

17 Mar, 26

Salary

0.0

Posted On

17 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Python, SQL, Feature Engineering, Experimentation, Statistical Evaluation, MLOps, Data Quality, Collaboration, Mentoring, Data Analysis, Model Development, Hyperparameter Tuning, Data Pipelines, AI Tools

Industry

Software Development

Description

Upwork Inc.’s (Nasdaq: UPWK) family of companies connects businesses with global, AI-enabled talent across every contingent work type including freelance, fractional, and payrolled. This portfolio includes the Upwork Marketplace, which connects businesses with on-demand access to highly skilled talent across the globe, and Lifted, which provides a purpose-built solution for enterprise organizations to source, contract, manage, and pay talent across the full spectrum of contingent work. From Fortune 100 enterprises to entrepreneurs, businesses rely on Upwork Inc. to find and hire expert talent, leverage AI-powered work solutions, and drive business transformation. With access to professionals spanning more than 10,000 skills across AI & machine learning, software development, sales & marketing, customer support, finance & accounting, and more, the Upwork family of companies enables businesses of all sizes to scale, innovate, and transform their workforces for the age of AI and beyond. Since its founding, Upwork Inc. has facilitated more than $30 billion in total transactions and services as it fulfills its purpose to create opportunity in every era of work. Learn more about the Upwork Marketplace at Upwork.com As a Lead Data Scientist, you’ll be a key member of our centralized Data Science team within the Finance organization, driving the analytics and modeling that power Upwork’s growth and marketplace efficiency. You’ll lead high-impact projects end-to-end—framing ambiguous problems, developing robust models, and turning insights into actionable decisions across domains like search relevance, pricing, macro-impact on business, supply-demand balance, and risk. You’ll spend most of your time advancing core data science work: exploratory data analysis, model design, feature engineering, and experimentation. The rest of your focus will go toward building the frameworks, tools, and workflows that make Data Science scalable—accelerating experimentation, improving data quality, and ensuring our analytical systems are trusted and repeatable. This role will partner closely with stakeholders across the business to understand their goals, uncover pain points, and translate those needs into actionable data science solutions. You’ll take ownership of a key problem space, driving clarity on problem definition, scoping opportunities, and executing the technical work end-to-end to deliver measurable impact. This role blends technical depth with influence and leadership. You’ll create clarity across functions, mentor data scientists, and raise the bar for analytical rigor and impact while staying close to the data yourself. Responsibilities: Lead data science initiatives from exploration through delivery, defining metrics and success criteria that connect your work to measurable business outcomes. Develop statistical and machine learning models to optimize marketplace systems such as search, pricing, and recommendations, conducting rigorous offline experimentation to select features, tune hyperparameters, and validate model performance. Build and maintain high-quality feature pipelines with clear data contracts, lineage documentation, and data-quality monitoring in collaboration with Data Engineering and Infrastructure. Set up and improve DS workflows: experiment tracking, reproducibility, and CI/CD practices that shorten time-to-insight and strengthen reliability. Mentor peers through code reviews, model evaluations, and collaborative problem solving, helping elevate the team’s technical standards. Translate complex analyses into insights that influence product, finance, and executive decisions. What it takes to catch our eye 5+ years of experience in Data Science, Applied Science, or a similar role delivering data-driven solutions at scale. Advanced Python and SQL skills, including data manipulation (pandas, NumPy, scikit-learn) and workflow tools such as Airflow, Dagster, or Spark. Familiarity with dashboarding tools such as HEX and Tableau is a strong plus. Comfort working with modern AI-assisted coding tools (e.g., ChatGPT, Cursor, Gemini) to accelerate development, prototype faster, and iterate on ideas (a.k.a vibe-coding). Deep understanding of experimentation and statistical evaluation, with a focus on actionable business impact. Hands-on experience with model development and evaluation, including feature engineering, calibration, and error/bias analysis. Practical MLOps expertise—implementing experiment tracking, reproducibility practices, environment management, containerization (e.g., Docker), version control, and leveraging feature stores or model registries. Collaborative communicator who thrives in cross-functional environments and enjoys mentoring others while staying hands-on with the work. This position will initially be employed through a partner to ensure a seamless hiring process while we establish the hub. Once the hub is established, there may be opportunities to transition to employment with Upwork depending on business needs and other requirements. While employed by the partner, you’ll work as part of Upwork’s team, with access to our resources, culture, and growth opportunities. To learn more about how Upwork processes and protects your personal information as part of the application process, please review our Global Job Applicant Privacy Notice

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Responsibilities

Lead data science initiatives from exploration through delivery, defining metrics and success criteria that connect your work to measurable business outcomes. Develop statistical and machine learning models to optimize marketplace systems such as search, pricing, and recommendations.