Data scientist workforce, product, Operations analytics *** Direct end client ***

Data scientist workforce, product & Operations Analytics

Position Overview

In this role, you will support the operations team’s strategy determining the optimal mix of internal, third-party, domestic, and international workforce resources supporting our financial and software platforms.

Your primary responsibility will be to go beyond static dashboards to build predictive and explanatory models that project the operational and financial impact of these workforce shifts. You will evaluate critical trade-offs between labor cost premiums and customer experience outcomes (such as handle times, transfer rates, and resolution rates) to drive rapid, data-backed strategic decisions.

Key Responsibilities

• Perform Advanced Business Analysis: Formulate data-backed strategies using statistical analysis, predictive modeling, and data mining to drive step-function growth and increase customer benefits.
• Evaluate Strategic Workforce & Labor Models: Analyze trade-offs between internal vs. external and credentialed vs. non-credentialed workforce segments. Determine if paying a premium for specific models (such as onshore resources) is net-neutral or positive by evaluating impacts on handle times, resolve rates, and contact volume.
• Consolidate and Mix-Adjust Metrics: Combine isolated performance indicators (including customer satisfaction, transfer rates, average handle time, and financial performance) into a single, holistic topology view. Apply mix-adjustments to control for contact complexity, volume, and tenure to ensure fair comparisons across distinct labor pools.
• Build Data Pipelines and Staging Tables: Access the corporate data lake to extract and transform raw data into custom staging tables within an orchestration framework ensuring other teams can easily access consolidated topology data.
• Analyze Multimodal Data: Merge highly structured, tabular databases with unstructured datasets (such as customer chat transcripts, calls, and written anecdotes) to provide a complete picture of customer pain points.
• Collaborate and Align Stakeholders: Work directly with cross-functional working teams—including finance, operations, and external data science groups. Align on data inputs and sources up front to ensure business reviews focus on debating outputs and strategic actions rather than arguing over data validity.
• Support Agile, Ad Hoc Analysis: Utilize modern AI integrations (such as Claude and GitHub workflows) alongside traditional platforms to rapidly generate insights and solve immediate operational questions.

Required Skills & Experience

• Experience: 5+ years of experience in data science, workforce analytics, or product analytics (preferably in a fintech or financial services environment).
• Education: BS or MS degree in Statistics, Mathematics, Computer Science, or a related quantitative field.
• Statistical Strategy & Modeling: Strong background in statistical modeling, hypothesis generation, and experimental design. Demonstrated capability to operate independently to solve open-ended strategic problems rather than simply executing tasks.
• Causal Inference: Hands-on experience with advanced causal inference techniques, specifically propensity score matching, difference-in-differences (DiD), and synthetic control methods.
• Programming & Tools:
  • Advanced SQL skills for data extraction, manipulation, and pipeline creation.
  • Strong Python proficiency (including NumPy, Pandas, Scikit-learn, and related libraries).
  • Ability to use Generative AI and modern developer tools (e.g., Claude, GitHub integrations) to accelerate analytical workflows.
• Data Visualization: Experience with scalable BI and reporting platforms, with a strong preference for Qlik Sense or Tableau.
• Communication: Outstanding communication skills. Must be able to walk non-technical working-level teams (finance, business partners) through complex data logic to build consensus and drive swift decisions.

Preferred Qualifications

• Call Center Domain Expertise: Prior experience analyzing contact center or customer support metrics (e.g., Average Handle Time/AHT, resolution rates, transfer rates, conversion) is highly desirable.

Workforce Analytics, Operations Research, Causal Inference, Propensity Score Matching, Difference-in-Differences, Statistical Modeling, Data Lake, Superglue, Qlik Sense, Tableau, Multimodal Data, Predictive Modeling, FinTech, Contact Center Metrics, AHT, Workforce Topology, Staging Tables