- 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.
