Senior Customer Lifetime Value Modeling Specialist

BIM Pure; Abu Dhabi; A distancia; AED 120.000 - 180.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

100% remote

Competitive compensation

Professional development

Cross-functional collaboration

Descripción de la vacante

BIM Pure is seeking a Senior Customer Lifetime Value Modeling Specialist to design and validate CLV models using historical customer and transaction data. You will forecast long-term revenue, retention, and profitability while collaborating with marketing, finance, product, and data engineering teams.

You will translate model outputs into actionable business recommendations, monitor performance, and advance data-driven decision making in a fully remote setting.

Formación

• Degree in statistics, mathematics, economics, data science, finance or related field.
• Experience in customer analytics and predictive/financial modeling.
• Experience building CLV or customer profitability models.
• Strong Python or R, SQL, and ML knowledge.
• Ability to explain complex models to non-technical stakeholders.

Responsabilidades

• Design, develop, and maintain CLV models using historical data.
• Estimate future revenue, profitability, retention, and purchase frequency.
• Develop models for customer-level and segment-level CLV forecasting.
• Analyze CAC, retention, repeat purchase behavior, and contribution margins.
• Apply statistical methods and ML to improve CLV estimation.
• Perform customer segmentation based on predicted value and profitability.
• Develop cohort analyses to evaluate value over time periods.
• Incorporate churn probability, discount rates, and purchase patterns.
• Validate model assumptions, data quality, and forecast reliability.
• Conduct sensitivity analysis and scenario modeling.
• Identify data gaps, biases, and limitations affecting CLV estimates.
• Monitor model performance and recalibrate as needed.
• Collaborate with marketing, finance, product, customer experience, and data engineering teams.
• Translate CLV insights into recommendations for acquisition, retention, loyalty, and resource allocation.
• Support measurement of marketing campaign effectiveness using value metrics.

Conocimientos

Statistical forecasting

Machine learning

Data analysis

Communication

Problem solving

Educación

Bachelor's or Master's in a quantitative discipline

Herramientas

Python

R

SQL

BI tools

Descripción del empleo

Job Title: Senior Customer Lifetime Value Modeling Specialist

Location: United Arab Emirates (Remote)

Employment Type: Full-Time

Work Arrangement: 100% Remote — Work From Home

Level: Senior

About the Role

We are seeking an experienced Senior Customer Lifetime Value Modeling Specialist to develop, validate, and optimize analytical models that estimate the long-term economic value of customers. The successful candidate will combine statistical modeling, predictive analytics, and customer behavior analysis to forecast future revenue, assess retention patterns, and support strategic decisions across marketing, customer acquisition, and retention. This role requires strong quantitative expertise and the ability to translate complex model outputs into practical business recommendations.

Key Responsibilities

• Design, develop, and maintain customer lifetime value (CLV) models using historical customer, transaction, and engagement data.
• Estimate future customer revenue, profitability, retention, purchase frequency, and expected relationship duration.
• Develop predictive models for customer-level and segment-level lifetime value forecasting.
• Analyze customer acquisition costs, retention rates, repeat purchase behavior, and contribution margins.
• Apply statistical methods and machine learning techniques to improve CLV estimation and forecasting accuracy.
• Perform customer segmentation based on predicted lifetime value, behavioral characteristics, and profitability.
• Develop cohort analyses to evaluate customer value over different acquisition periods and lifecycle stages.
• Incorporate churn probability, discount rates, purchase patterns, and other relevant variables into valuation frameworks.
• Validate model assumptions, data quality, predictive performance, and forecast reliability.
• Conduct sensitivity analysis and scenario modeling to assess changes in customer behavior and business conditions.
• Identify data gaps, potential biases, and methodological limitations affecting CLV estimates.
• Monitor model performance and recalibrate models when customer behavior or market conditions change.
• Collaborate with marketing, finance, product, customer experience, and data engineering teams.
• Translate CLV insights into recommendations for customer acquisition, retention, loyalty, and resource allocation strategies.
• Support the measurement of marketing campaign effectiveness using customer value and profitability metrics.
• Maintain technical documentation covering methodologies, assumptions, model versions, validation results, and limitations.
• Present analytical findings and strategic recommendations to senior management and relevant stakeholders.

Qualifications and Experience

• Bachelor's or master's degree in Statistics, Mathematics, Economics, Data Science, Finance, Computer Science, or a related quantitative discipline.
• Significant professional experience in customer analytics, predictive modeling, financial modeling, or business intelligence.
• Demonstrated experience developing customer lifetime value or customer profitability models.
• Strong understanding of statistical forecasting, regression analysis, probability modeling, and machine learning.
• Proficiency in Python or R and relevant statistical or machine learning libraries.
• Advanced SQL skills and experience working with large-scale customer and transaction datasets.
• Knowledge of customer segmentation, cohort analysis, retention modeling, and churn prediction.
• Understanding of revenue forecasting, contribution margins, customer acquisition costs, and discounted cash flow concepts.
• Experience with model validation, sensitivity analysis, performance monitoring, and predictive accuracy assessment.
• Familiarity with data visualization and business intelligence tools.
• Ability to explain complex analytical concepts and model limitations to non-technical stakeholders.
• Strong quantitative reasoning, problem-solving, and attention to detail.
• Excellent communication and stakeholder management skills.
• Ability to work independently and manage analytical projects in a fully remote environment.

What We Offer

• 100% remote working arrangement within the stated employment framework.
• Competitive compensation based on qualifications and professional experience.
• Opportunities to contribute to strategic customer analytics and predictive modeling initiatives.
• Professional development and continuous learning opportunities.
• Collaborative environment with international and cross-functional teams.