Data Scientist – AI Enabled Call Center Efficiency and CEX

Location- Hybrid, Jersey City, NJ
Salary- $70- $115k plus bonus
For more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits
The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

We are seeking a highly motivated Sr. Data Scientist to lead end-to-end Next Best Action (NBA) strategy, delivery, and optimization initiatives. This role combines advanced analytics, business problem-solving, campaign decisioning, and operational ownership to drive personalized member engagement across lines of business.

The ideal candidate will be equally comfortable with analytics and execution, owning the full NBA lifecycle—from strategy translation and targeting logic to deployment, measurement, and continuous optimization.

Success Profile

The successful candidate will demonstrate:

• Strong business problem-solving capabilities alongside technical expertise.
• Ownership mentality for both analytics delivery and campaign operations.
• Ability to balance modeling and AI-driven insights (approximately 50%) with execution, delivery ownership, and NBA operations (approximately 50%).
• Excellent stakeholder communication and cross-functional collaboration skills.

Key Responsibilities

• Own the complete NBA delivery lifecycle, including strategy, design, development, testing, deployment, measurement, and ongoing optimization.
• Translate business objectives into actionable inclusion/exclusion criteria, targeting logic, and decision strategies.
• Develop and maintain SQL/BigQuery-based data pipelines supporting campaign targeting, sizing, execution, and reporting.
• Build and enhance measurement frameworks to evaluate campaign effectiveness, business outcomes, and member engagement.
• Perform ad hoc analyses to identify optimization opportunities and support stakeholder decision-making.
• Configure and support NBA implementations within Pega, including launch readiness and process validation.
• Conduct campaign QA, operational monitoring, and issue resolution to ensure successful NBA execution.
• Partner with business, product, and technology teams to drive data-informed decisioning and recommendation strategies.
• Support experimentation and test-and-learn initiatives, including A/B testing and performance measurement.
• Leverage predictive models, AI/ML, and LLM-based inference capabilities where appropriate to improve decisioning effectiveness.

Required Qualifications

• Bachelor's or Master's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
• Advanced proficiency in:
• SQL
• Google Cloud Platform (GCP)
• BigQuery
• Python
• Hands-on experience developing and managing BigQuery pipelines and analytical workflows.
• Strong understanding of campaign targeting, audience segmentation, recommendation systems, and decision strategies.
• Experience designing and implementing measurement frameworks and performance analytics.
• Ability to translate complex business problems into scalable analytical solutions.

Preferred Qualifications

• Experience in healthcare analytics, member engagement, or payer organizations.
• Knowledge of experimentation methodologies, uplift modeling, ranking/recommendation systems, and product analytics.
• Exposure to machine learning and Generative AI/LLM inference use cases.
• Experience supporting operational campaign delivery and production decisioning environments.
• Experience with Pega Decisioning or related campaign orchestration platforms.
• Strong experience in Next Best Action (NBA), campaign management, decisioning, or customer engagement analytics.