Data Scientist

About the Role

We are looking for a Senior Data Scientist to join a customer analytics engagement. In this role, you will apply machine learning, statistical modeling, and customer analytics to help the client better understand customer value, customer behavior, and movement across value segments.

You will work on problems involving customer lifetime value, customer segmentation, value transitions, causal analysis, and early-stage next-best-action recommendations.

This is an evolving engagement, so we are looking for someone who combines strong technical data science skills with business judgment, curiosity, and a proactive approach to solving ambiguous problems.

What You'll Do

• Build and apply machine learning models to classify customers into low-, medium-, and high-value segments and estimate transitions between these states.
• Develop Customer Lifetime Value (CLV) and customer value models to estimate current and future customer value.
• Analyze customer behavioral and transactional data to identify patterns, drivers, and opportunities to strengthen long-term customer relationships.
• Apply causal modeling, experimentation, and related analytical techniques to understand which customer behaviors or interventions influence customer value.
• Contribute to an initial Next Best Action (NBA) proof of concept to identify strategic opportunities for customer engagement and personalization.
• Develop customer segmentation and behavioral models to identify meaningful customer groups and their characteristics.
• Translate business and marketing questions into practical data science and machine learning approaches.
• Perform exploratory data analysis, feature engineering, model development, validation, and interpretation.
• Communicate analytical findings and model outputs clearly to both technical and non-technical stakeholders.
• Collaborate with Data Scientists and Data Engineers to leverage data from a unified customer record.
• Work in an evolving client environment, proactively identifying opportunities, proposing analytical approaches, and adapting to changing business priorities.
• Connect technical analysis to business outcomes and help stakeholders understand why the model or analysis matters.