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.
What We're Looking For
Required
- 3+ years of hands-on Data Science / Machine Learning experience.
- Strong programming skills in Python.
- Strong SQL skills and experience working with large datasets.
- Hands-on experience developing and applying machine learning models to business problems.
- Strong understanding of:
- Predictive modeling
- Feature engineering
- Model evaluation
- Statistical analysis
- Customer/behavioral analytics
- Experience with one or more of:
- Customer segmentation
- Customer Lifetime Value / customer value modeling
- Customer behavior modeling
- Churn / retention modeling
- Propensity modeling
- Causal modeling
- A/B testing / experimentation
- Uplift modeling
- Next Best Action / recommendation approaches
- Ability to translate complex analytical problems into practical solutions and communicate insights clearly.
- Strong business judgment and ability to connect analytical outputs to measurable business outcomes.
- Comfortable working with ambiguity and evolving requirements.
- Strong collaboration and stakeholder management skills.
Preferred Qualifications
- Experience in customer analytics, marketing analytics, consumer analytics, loyalty, or CRM analytics.
- Experience working with transactional and behavioral customer data.
- Experience with customer value segmentation or movement between customer segments.
- Experience with causal inference, uplift modeling, experimentation, or treatment-effect analysis.
- Experience with propensity models, personalization, recommendations, or next-best-action frameworks.
- Experience in Retail, CPG, Consumer, E-commerce, Loyalty, or Marketing Analytics.
- Experience with PySpark, Databricks, AWS, Azure, or other cloud/data platforms.
- Experience communicating analytical recommendations to senior business stakeholders.
What Success Looks Like
In this role, success means being able to move beyond simply building models. You will be expected to:
- Understand the business problem behind the analytical request.
- Build models that provide meaningful insight into customer value and behavior.
- Identify what causes or contributes to changes in customer value.
- Translate analytical findings into clear business recommendations.
- Proactively identify opportunities to improve the customer analytics approach.
- Work effectively with Data Scientists, Data Engineers, and client stakeholders as the engagement evolves.
Why Blend
At Blend, you will have the opportunity to work on meaningful, real-world data science problems with leading global organizations. You will collaborate with experienced data scientists, engineers, and business stakeholders while solving problems where technical depth, business thinking, and the ability to operate in ambiguity are equally important.