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Crystal Equation Corporation seeks a Data Analyst V for its Customer Experience team, fully remote in the United States. You will analyze large, complex datasets, apply advanced statistical modeling, and craft data stories to drive customer-centric improvements across journeys.
Responsibilities include synthesizing signals from surveys and support data, using ML/NLP on interactions, and building a comprehensive CX metric scorecard.
Data Analyst V Remote Summary: Our customer experience team is dedicated to improving customer experiences across the entire customer journey. As a Customer Experience Analyst, you will be part of a global practice focused on understanding and learning from our customers. You will leverage customer insights to drive business improvements and promote customer-centric decision-making throughout the organization. You will play a pivotal role in boosting customer satisfaction and loyalty by analyzing customer signals, identifying areas for improvement, and collaborating with cross-functional teams to develop and implement strategies that enhance the overall customer experience. By combining your strong background in CX/VoC, expertise in data analysis and storytelling, and passion for customer centricity, you will help shape the future of our customer experience program and contribute to the growth and success of our organization.
Work with large and complex datasets to solve challenging problems using various analytical and statistical approaches. Synthesize and analyze customer signals from multiple sources (e.g., surveys, digital analytics, behavioral, and operational data) across end-to-end customer journeys to identify trends, pain points, and areas for improvement. Apply advanced statistical modeling, machine learning, and natural language processing (NLP) to analyze large-scale customer support interactions (e.g., chat logs, call transcripts, support tickets) and extract actionable insights. Use clustering and segmentation techniques to identify common customer issues and recommend targeted solutions or self-service resources. Integrate customer feedback data with other transactional, operational, and behavioral data sources to create a comprehensive picture of customer experience drivers. Use various statistical techniques to surface and rank the drivers of core and inferred metrics, separating meaningful signal from noise. Define, test, and launch a comprehensive customer support and CX metric scorecard that tracks key performance indicators, uncovers underlying operational and experiential drivers across digital, AI, and human assisted support. Demonstrate the ROI of CX by establishing the relationship between customer metrics and business outcomes. Design, prompt, and orchestrate AI systems to automate data analysis, synthesize insights, and execute multi-step analytical tasks (e.g., prompting agents to run analysis, clean datasets, or build visualizations). Advocate for customers and influence corrective actions through periodic and ad-hoc reporting, proactively evangelizing insights among key stakeholders. Communicate complex technical concepts to non-technical stakeholders.
Degree in Analytics, Statistics, Mathematics, Computer/Data Science, Engineering, or a related field. 8+ years of experience in data analytics, data science, or related fields. 2+ years of experience in customer experience, customer support, or customer insights analytics. Proficiency in crafting compelling stories using complex data sources to provide actionable insights tailored to stakeholders’ needs. Experience working with Customer Experience or Voice of Customer metrics (NPS, CSAT, etc.), surveys, and customer feedback. Strong programming skills in Python, R, and SQL. Strong proficiency in utilizing statistical analytics techniques (e.g., correlation analysis, multivariate regression, and feature-importance methods). Proficiency in conducting predictive analytics or running ML/AL techniques. Proficiency in AI-powered tools and demonstrated working knowledge of Generative AI technologies for analytics and reporting (e.g., LLMs and AI agents). Demonstrated ability to work collaboratively with cross-functional teams.
Master's or Ph.D. Degree in a quantitative field. Familiarity with consumer electronics or retail business. Experience with machine learning libraries such as Pandas, scikit-learn, TensorFlow, or PyTorch.
Pay range is $89 - $94 per hour with full benefits available, including paid time off, medical/dental/vision/life insurance, 401K, parental leave, and more. Our compensation reflects the cost of labor across several US geographic markets. Pay is based on several factors including market location and may vary depending on job-related knowledge, skills, and experience.
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