Remote - Marketing Intelligence Analyst

World Business Lenders

3d•Remote

About The Position

World Business Lenders (WBL) is proud to offer short-term, real estate-backed commercial loans to a diverse range of small and medium-sized businesses across the United States, especially those who may find it challenging to secure traditional financing. Typically, the work schedule is from 9:00am to 6:00pm Eastern Time, Monday through Friday, though there might be times when additional hours are needed to meet business demands. We’re looking for someone with strong communication skills in both English, both spoken and written. Please note that all resumes should be submitted in English. This role is responsible for developing predictive models and forecasting frameworks that support marketing performance, customer acquisition, and market targeting, while also contributing to broader internal data science initiatives. The position combines hands-on statistical modeling and machine learning with applied business problem-solving to drive more effective, data-driven decision-making. The role will work closely with BI and analytics partners to ensure alignment between predictive outputs and reporting, helping translate model results (e.g., lead scoring, conversion propensity, and forecasting) into actionable insights. In parallel, this individual will support internal data science and modeling efforts, contributing to the development of proprietary analytics capabilities. The role is expected to initially focus on marketing-related modeling and progressively expand into deeper data science work over time. This position requires a solution-oriented mindset — someone who takes ownership, challenges assumptions, and actively improves processes rather than simply executing tasks.

Requirements

Bachelor’s or Master’s degree in Data Science, Statistics, Mathematics, or related field.
3 to 7 years of experience in data science, applied modeling, or advanced analytics.
Strong proficiency in Python and SQL
Proven experience building and validating predictive models in a business environment
Experience with statistical methods, machine learning techniques, and forecasting approaches
Strong problem-solving skills and ability to translate business problems into analytical solutions
Ability to communicate complex analytical concepts to non-technical stakeholders
Experience with customer acquisition cost modeling and marketing ROI analysis

Nice To Haves

Experience working with marketing, customer acquisition, or lead generation data
Familiarity with segmentation, lead scoring, or customer lifecycle modeling
Experience with experimentation and causal inference methods
Exposure to market-level modeling or customer targeting strategies
Experience contributing to production-level or internal data science projects

Responsibilities

Develop, validate, and maintain predictive models including lead scoring, conversion propensity, and funnel optimization
Develop tiering models to prioritize leads by conversion probability
Build forecasting frameworks for lead volume, conversion rates, and pipeline or revenue projections
Build channel performance models to optimize budget allocation across paid search, email, ISO partners, etc.
Monitor model performance and iterate to improve accuracy and business impact
Develop models to evaluate and prioritize markets based on expected performance and ROI
Analyze geographic, industry, and segment-level trends to identify growth opportunities
Support data-driven recommendations on market targeting and resource allocation
Support campaigns/future product launches by forecasting lead volume, conversion rates, and CAC by channel
Design and analyze experiments (A/B testing, cohort analysis) and evaluate incremental impact
Apply statistical and machine learning techniques to solve marketing and business problems
Conduct deep-dive analyses to support strategic and operational decisions
Contribute to internal data science and modeling initiatives, including proprietary or internal IP work
Collaborate with Data Engineering to structure and prepare datasets for modeling
Partner with BI and analytics teams to ensure model outputs are integrated into reporting and usable by stakeholders