Data Scientist Operations, product & Workforce Analytics

Position Overview

In this role, you will lead the data strategy for our global workforce determining the optimal mix of internal, third-party, domestic, and international resources.

Your primary responsibility will be to model the operational and financial impacts of moving volume across geographies and labor models. Rather than just delivering static dashboards, you will build models that enable stakeholders to make rapid, strategic decisions.

Required Tools & Technical Stack

You will be expected to operate within the following modern analytics environment:

• Agile Analytics & Integration: Extensive, hands-on use of Claude and GitHub integration to stand up solutions and perform rapid, ad-hoc analysis.
• Data Lake & Pipeline Staging: Capability to access large-scale corporate Data Lakes and build, transform, and house custom staging tables within a data environment to make cleaned topology data accessible to other working teams.
• Reporting & BI Visualization: Qlik Sense as the primary, scalable reporting tool, alongside legacy Tableau data when necessary.
• Core Programming: Strong proficiency in SQL and/or Python (with comfort using AI-assisted tools to bridge language gaps).
• Statistical Strategy: Mastery of advanced causal inference techniques, specifically propensity score matching and difference-in-differences (DiD).

Core Responsibilities

As the primary Data Scientist your work will revolve around measuring, normalizing, and optimizing the following key performance indicators:

1. Modeling Labor Costs vs. Customer Experience Trade-offs

• Analyze the financial and operational trade-offs of paying premium labor rates (e.g., domestic or credentialed experts) versus utilizing lower-cost general product support.
• KPIs:
• Average Handle Time (AHT): Assess if credentialed/onshore models shorten contact lengths or if more thorough answers warrant longer handle times.
• Resolve Rate / Resolution Rate: Verify if premium labor models yield higher first-contact resolution.
• Customer Experience (CX) & CSAT: Connect qualitative customer sentiment with operational outcomes.

1. Mix-Adjusting and Normalizing Performance Metrics

• Consolidate disparate operational databases from various business lines into a single, holistic topology view. Apply advanced statistical "mix-adjustments" to ensure fair performance comparisons across diverse labor pools.
• KPIs:
• Contact Complexity & Contact Volume: Adjust raw performance data to control for the difficulty of the cases handled and the volume of contacts routed to specific geographies or tiers.
• Tenure: Account for employee tenure variables when comparing internal vs. external performance metrics.
• Transfer Rate & Conversion Rate: Normalize and track transfer and conversion rates across different geographic configurations.

1. Impact Projection & Scenario Modeling

• Create predictive models to simulate major workforce shifts (e.g., shifting specific volumes from domestic to international partners or third-party vendors).
• KPIs:
• Net-Neutral Impact: Model costs and operational efficiencies side-by-side to ensure geography shifts remain cost-neutral or positive overall.
• Operational Health Forecasts: Project how relocations will impact localized service delivery metrics, AHT, and customer satisfaction.

1. Data Ingestion, Ingest and merge highly structured, tabular performance databases with unstructured customer sentiment datasets such as customer chat transcripts, phone call logs, and written anecdotes to validate quantitative metrics with voice-of-the-customer insights

Required Qualifications

• Experience: 7+ years of experience in data science, workforce analytics, or operational/product analytics (fintech or SaaS experience preferred).
• Education: BS or MS degree in Statistics, Mathematics, Computer Science, or a related quantitative field.
• Data Multimodality: Proven ability to merge highly structured relational databases with unstructured data sources (e.g., customer chat transcripts, calls, and written anecdotes) to validate hard metrics with voice-of-the-customer insights.
• Autonomy: Strong ability to operate independently to solve open-ended strategic problems rather than simply executing prescribed tasks.
• Communication: Outstanding communication skills, with the ability to explain complex statistical logic to non-technical working groups (including finance and operations partners).

Data Scientist, SQL, Python, Superglue, Data Lake, Qlik Sense, Tableau, GitHub, Claude, Propensity Score Matching, Difference-in-Differences, Causal Inference, Average Handle Time, AHT, Resolution Rate, Transfer Rate, Conversion Rate, Contact Complexity, Contact Volume, Workforce Analytics, Operations Research, Sourcing Strategy, Statistical Modeling