Description
Thomson Reuters Legal is seeking a Lead Data Scientist to help power data-driven growth and adoption of 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 the Customer Data Platform (Treasure Data) and downstream activation workflows.
Responsibilities
- 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.
Requirements
- 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.
What's in it For You?
- Hybrid Work Model: flexible hybrid working environment
- Flexibility & Work-Life Balance: supportive workplace policies
- Career Development and Growth: culture of continuous learning and skill development
- Industry Competitive Benefits: comprehensive benefit plans
- Culture: globally recognized, award-winning reputation for inclusion and belonging
- Social Impact: opportunities to make an impact in the community
- Making a Real-World Impact: helping customers pursue justice, truth, and transparency