JOB SUMMARY: The Data Scientist, Customer Care Analytics is responsible for developing, validating, deploying, and monitoring advanced analytical, machine learning, and Generative AI solutions that improve customer outcomes, operational performance, employee productivity, and business decision-making.
This role partners closely with Customer Care, Collections, Billing, Workforce Management, Technology, and Business Leadership teams to transform data into actionable insights and scalable solutions. The successful candidate will identify opportunities to leverage reporting, advanced analytics, predictive modeling, optimization, Large Language Models (LLMs), and other AI technologies to address complex business challenges.
The role is accountable for the end-to-end lifecycle of analytical and AI solutions, including business problem definition, data acquisition, model development, validation, deployment, monitoring, governance, and business adoption. In addition to advanced analytical work, the role will contribute to reporting, dashboarding, and decision-support solutions that enable data-driven decision-making across Customer Care operations. The role aligns well with the capabilities of advanced analytics, machine learning, model monitoring, validation, and decision-support solutions described within existing enterprise analytics practices.
KEY RESPONSIBILITIESData Science & AI Solutions- Develop and deploy statistical, machine learning, forecasting, optimization, and Generative AI solutions that improve customer outcomes and operational performance.
- Translate business problems into analytical approaches and actionable recommendations.
- Evaluate when reporting, advanced analytics, machine learning, or LLM-based solutions are the most appropriate approach.
Model Development, Validation & Monitoring- Design, build, validate, and maintain predictive, forecasting, and AI models.
- Establish model performance standards, monitoring frameworks, and governance controls.
- Identify model drift, performance degradation, and opportunities for retraining or enhancement.
Data & Technical Delivery- Acquire, integrate, and prepare data from multiple sources for analytical and AI applications.
- Develop scalable analytical workflows, data pipelines, and reusable data assets using modern analytics tools and platforms.
- Apply best practices for testing, documentation, version control, and deployment.
Business Partnership & Innovation- Partner with business leaders and stakeholders to identify opportunities for analytics and AI to create business value.
- Communicate analytical findings and recommendations to technical and non-technical audiences.
- Evaluate emerging technologies and drive innovation through advanced analytics, AI, and automation initiatives.
Reporting & Decision Support- Develop dashboards, reports, scorecards, and data visualizations that support operational and strategic decision-making.
- Translate analytical outputs into clear business insights and performance measures.
- Ensure reporting accuracy, consistency, and alignment with business definitions.
Minimum Qualifications- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Economics, Operations Research, or related quantitative discipline.
- 3+ years of experience in Data Science, advanced analytics, machine learning, forecasting, or related analytical roles.
- Experience developing predictive, forecasting, optimization, or statistical models in a business environment.
- Strong proficiency with SQL and Python.
- Experience working with large and complex datasets.
- Experience applying statistical analysis and quantitative problem-solving techniques.
- Experience communicating analytical findings to non-technical stakeholders.
- Experience developing dashboards or reports using Power BI, Tableau, or similar tools.
Preferred Qualifications:- Experience with Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI assistants.
- Experience with Azure OpenAI, Databricks, MLflow, Azure AI, or similar cloud-based AI platforms.
- Strong understanding of statistics, machine learning, forecasting, and Generative AI technologies.
- Experience with model validation, model governance, and model monitoring practices.
- Experience with forecasting, optimization, contact center analytics, workforce management, collections, or customer care operations.
- Experience deploying and supporting production machine learning solutions.
- Knowledge of responsible AI, model explainability, and AI governance principles.
Additional Knowledge, Skills & Abilities- Ability to determine the most appropriate solution approach, whether reporting, analytics, machine learning, optimization, or AI.
- Strong business acumen and problem-solving skills.
- Excellent communication, data storytelling, and presentation skills.
- Strong technical writing and documentation capabilities.
- Ability to manage multiple priorities in a fast-paced environment.
- Continuous learning mindset with interest in emerging AI and analytics technologies.
