Modeler – Operations & Call Center Efficiency

Develop and optimize machine learning models for call deflection prediction, intelligent routing, and automated quality monitoring.
Apply NLP techniques to analyze call transcripts and digital interaction logs to classify intent, detect topics, and surface automation opportunities.
Perform feature engineering on structured and unstructured operational data sources including CRM records, call logs, and interaction metadata.
Support end-to-end model lifecycle activities including data preparation, model training, evaluation, documentation, and performance monitoring.
Design and execute A/B tests or champion-challenger evaluations to measure model impact on operational KPIs.
Collaborate with contact center operations and technology teams to understand workflows and integrate model outputs into existing systems.
Prepare clear model documentation covering methodology, assumptions, performance metrics, and monitoring plans.
Monitor deployed models for drift and degradation, escalating issues and recommending retraining or recalibration as needed.
Stay current on advances in conversational AI, NLP, and operational analytics.
Qualifications & Experience
2–9 years of experience in data science, analytics, or machine learning with exposure to operational or contact center domains.
Working knowledge of NLP techniques including text classification, entity extraction, and sentiment analysis.
Proficiency in Python; familiarity with libraries such as scikit-learn, spaCy, Hugging Face, pandas, and numpy.

Experience with SQL and working with large, complex operational datasets.
Understanding of contact center metrics such as handle time, first-call resolution, CSAT, and deflection rates is preferred.
Familiarity with model validation concepts, performance evaluation frameworks, and documentation standards.
Strong analytical and problem-solving skills with a practical, solutions-oriented mindset.
Effective communication skills for presenting analytical findings to both technical peers and operational stakeholders.
Bachelor's degree (Master's preferred) in Computer Science, Statistics, Engineering, or a related quantitative discipline.
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.