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Added 13 mins ago
CO
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- Full-time
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Thomson Reuters

Lead Data Scientist, Customer & Growth Analytics

Toronto, ON, Canada
Canada, Toronto, Ontario, Canada

Full-time

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Job details

Job type: Full-time
Apply by: Oct 19, 2026
Experience: 5+ years of experience in data science, analytics, or applied machine learning in a B2B SaaS or subscription-based environment.
Posting language: English
Posted: Oct 5, 2026

Original job description

About the Role

Thomson Reuters Legal is seeking a Lead Data Scientist to help power data-driven growth and adoption of our AI-enabled legal solutions. In this role, you will design, build, and maintain predictive models - including propensity-to-upgrade models - that identify customers most likely to expand into AI products such as CoCounsel.

You will work closely with Lifecycle Marketing, Marketing Operations, and Customer Success to ensure insights and models are not only accurate, but actionable - embedded directly into our Customer Data Platform (Treasure Data) and downstream activation workflows. This is a highly collaborative, hands-on role focused on production-ready models that drive experimentation, pipeline development, and measurable customer value.

About the Role
As a Lead Data Scientist, you will

Design, build, and maintain predictive models that support customer expansion, including propensity-to-upgrade and engagement scoring models.
Use behavioral, usage, firmographic, and lifecycle data to identify signals that indicate readiness for AI adoption and expansion.
Partner with Lifecycle Marketing to translate model outputs into actionable segments and testable hypotheses across the customer lifecycle.
Collaborate closely with Marketing Operations to operationalize models within the Customer Data Platform (Treasure Data) for activation and measurement.
Develop features and datasets using product usage data, campaign engagement, learning activity, and customer attributes.
Validate, monitor, and continuously improve model performance, ensuring accuracy, explainability, and alignment to business outcomes.
Support experimentation by defining success metrics, analyzing lift, and interpreting results to inform optimization decisions.
Document modeling approaches, assumptions, and outputs to enable transparency, reuse, and cross-functional understanding.
Work cross-functionally with Data Engineering, Product, Customer Success, and Commercial teams to ensure data quality and aligned outcomes.

About You:
You’re fit for the role of Lead Data Scientist if your background includes:

5+ years of experience in data science, analytics, or applied machine learning in a B2B SaaS or subscription-based environment.
Experience building predictive or classification models that influence customer growth, retention, or expansion decisions.
Strong proficiency in Python or similar data science tools, including feature engineering and model evaluation.
Demonstrated experience working with large, complex datasets such as product usage, behavioral logs, or campaign data.
Experience partnering with Marketing, Growth, or Customer Success teams to translate insights into action.
Familiarity with deploying or activating models within analytics platforms or CDPs; experience with Treasure Data or similar platforms is a plus.
Strong understanding of experimentation, model validation, and measuring impact using both statistical and business metrics.
Ability to clearly communicate technical concepts and insights to non-technical stakeholders.
Curiosity and ownership mindset, with a strong bias toward building models that are used and deliver measurable outcomes.

#LI-GK1

New Position: This position is open due to an existing vacancy to support our evolving business needs.

What’s in it For You?

Hybrid Work Model: We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.
Flexibility & Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow’s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.

Our use of AI within the recruitment process Thomson Reuters utilizes Artificial Intelligence (AI) to support parts of our global recruitment process. Unless you opt-out, our AI system will assess the information provided by you and compare it to the requirements listed for the role, and present the result to our recruitment personnel for further review. The AI system acts as a supporting tool, but there is always a human making the decision if you will be considered for the role

Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.

For Ontario, Canada, the base compensation range for this role is $140,600 CAD - $190,600 CAD.

Base pay is positioned within the range based on several factors including an individual’s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.
This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.


About Us

Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.

We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.

As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.

We also make reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs in accordance with applicable law. More information on requesting an accommodation here.

Learn more on how to protect yourself from fraudulent job postings here.

More information about Thomson Reuters can be found on thomsonreuters.com

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More about this job

Responsibilities

The Lead Data Scientist will design, build, and maintain predictive models to identify customer expansion opportunities and AI adoption readiness. They will collaborate with marketing and product teams to operationalize these models within the Customer Data Platform to drive actionable insights.

Requirements

Candidates must have 5+ years of experience in data science or applied machine learning within a B2B SaaS environment. Proficiency in Python and experience with large, complex datasets and model deployment in analytics platforms are required.

Skills

Data science
Machine learning
Predictive modeling
Python
Feature engineering
Model evaluation
Customer analytics
Growth analytics
B2B SaaS
Customer data platform
Treasure data
Statistical analysis
Experimentation
Data visualization
Cross-functional collaboration

Benefits

Flexible vacation
Mental health days
Headspace app access
Retirement savings
Tuition reimbursement
Employee incentive programs
Volunteer days
Flexible work arrangements

Visa sponsorship

Not detected in the job text

Keywords

Data Science
Machine Learning
Predictive Modeling
Python
B2B SaaS
Customer Data Platform
Treasure Data
Propensity Modeling
Engagement Scoring
Lifecycle Marketing
Data Engineering
Experimentation
Statistical Metrics
Feature Engineering
Model Validation
AI Solutions
Growth Analytics
Customer Success
Marketing Operations
Data-driven Growth

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