ML Modeling

Modeler – Marketing Models
Modeler |Marketing Models | 3–6 Years of Experience
Position Overview
We are seeking a skilled Modeler to contribute to the development and optimization of marketing analytics models. You will work within a cross-functional team to build propensity models, next-best-action (NBA) engines, customer segmentation frameworks, and campaign response models that power data-driven marketing strategies. You will apply best practices in model explainability, fairness testing, and lifecycle governance to ensure high-quality, compliant model outputs.
Key Responsibilities
Develop and optimize propensity models, segmentation frameworks, and campaign response models using supervised and unsupervised machine learning techniques.
Conduct bias testing and implement model explainability methods (e.g., SHAP, LIME) to support model approval and governance workflows.
Perform exploratory data analysis and feature engineering to identify meaningful predictors of customer behavior.
Support the full model lifecycle including development, documentation, validation support, performance monitoring, and periodic re-validation.
Collaborate with marketing and data teams to understand business objectives and translate them into modeling requirements.
Prepare clear, concise model documentation including methodology overviews, performance summaries, and limitation disclosures.
Monitor deployed models for data drift, performance degradation, and champion-challenger evaluation.
Contribute to A/B test design and campaign measurement analyses to evaluate marketing effectiveness.
Stay current on advances in marketing data science, uplift modeling, and customer analytics.
Qualifications & Experience
3–6 years of experience in data science, analytics, or quantitative modeling with a focus on customer or marketing analytics.
Hands-on experience building and evaluating classification and regression models for propensity scoring or segmentation.
Proficiency in Python with working knowledge of libraries such as scikit-learn, XGBoost, LightGBM, pandas, and numpy.
Familiarity with model explainability tools (SHAP, LIME) and basic concepts of model fairness and bias testing.
Experience with SQL and large-scale data platforms for data extraction and feature preparation.
Understanding of model validation principles and documentation standards.
Strong analytical and problem-solving skills with attention to detail.
Effective written and verbal communication skills for presenting model results to both technical and business stakeholders.
Bachelor's degree (master's preferred) in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
Model Lifecycle & Governance
This role supports end-to-end model lifecycle management. Responsibilities encompass model development, independent validation and assessment, performance optimization, monitoring, documentation, and governance — ensuring all models adhere to applicable standards and remain fit-for-purpose throughout their operational life.

Work arrangement
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