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# Senior Data Scientist - Member Experience

- **Company:** The Ladders
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $172,550.0 - $203,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Information Engineering, Data Transformation, Data Systems, Python (Programming Language), Machine Learning, Natural Language Processing, Operational Data Store, SQL Databases, Large Language Models, Snowflake, Model Validation, Data Layers, Tools for Reporting, Databricks
- **Published:** September 5, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pho4vuib7n

## About the Role

* 6+ years of experience in data science, machine learning, or advanced analytics
 * Strong proficiency in SQL and Python for analytical model building
 * Experience with NLP or LLM-based model evaluation and monitoring
 * Familiarity with forecasting and time series modeling for operational planning
 * Strong data engineering intuition for transforming data across systems
 * Experience with cloud platforms like Databricks, AWS, or Snowflake
 * Proven ability to communicate complex analytics to non-technical partners

## Description

This role will focus on building analytics and machine learning solutions that improve support operations and the overall member experience. You will partner closely with operations and member experience stakeholders to turn fragmented data into practical insights, scalable reporting, and production-ready systems. The position blends NLP, forecasting, and data engineering to strengthen support quality, capacity planning, and decision-making across the organization., * Build and maintain quality scoring classifiers for support interactions using NLP and LLM approaches

 * Design new support performance metrics that extend beyond traditional reporting
 * Enhance forecasting models for support capacity and staffing demand
 * Create a data layer linking support interactions to member lifecycle data
 * Collaborate with member experience and operational stakeholders to define problems and data solutions
 * Develop dashboards and reporting tools to visualize support quality and performance
 * Transform fragmented operational data into actionable insights and production-ready systems


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