This is an Applied Data Scientist / Customer Experience Analytics role focused on understanding customer behavior and digital experiences and translating analytical findings into business decisions and measurable outcomes.
The role combines exploratory data analysis, statistical methods, experimentation, behavioral analytics, and fit-for-purpose modeling to uncover insights and improve digital experiences and operations.
The ideal candidate will have hands-on experience with SQL, Python, applied statistics, customer behavior analytics, digital or product analytics, customer segmentation, feature adoption, and customer feedback analysis. The position involves collaboration with product, technology, and business stakeholders to turn complex business questions into actionable insights.
The primary focus is the business application of data science—not AI engineering, MLOps, model deployment, or architecture.
Analyze customer and member digital behavior using Python and SQL.
Conduct exploratory data analysis to identify patterns in product adoption, engagement, and customer experience.
Partner with business stakeholders and product teams to solve digital experience challenges.
Develop, automate, and maintain analytical solutions that provide actionable business insights.
Apply analytical and statistical methods to understand customer behavior, evaluate digital experiences, identify emerging trends, and support data-driven business decisions.
Apply text analytics, NLP, and AI-assisted techniques to summarize and classify customer feedback, identify sentiment and recurring themes, and surface customer friction points.
Analyze app reviews, surveys, contact center interactions, social media, and other digital feedback sources to conduct Voice of the Customer (VoC) and Voice of the Member (VoM) research.
Translate analytical findings into clear recommendations, dashboards, presentations, and data stories for business and executive audiences.
Analyze customer segmentation, behavioral patterns, product usage, feature adoption, and digital engagement.
Identify customer pain points, adoption drivers, and opportunities to improve digital experiences.
Communicate analytical findings and recommendations to technical and non-technical stakeholders.
Bachelor's degree in Data Science, Business Analytics, Statistics, Applied Mathematics, Economics, or another quantitative or analytical field.
1–5 years of experience conducting data science, business intelligence, analytics, statistical analysis, or related project work.
Proficiency using SQL for data extraction and analysis and Python for data preparation, exploratory analysis, statistical analysis, and modeling. Experience with R is beneficial.
Experience applying analytical and statistical methods such as:
Exploratory Data Analysis (EDA)
Hypothesis Testing
Regression Analysis
Forecasting
Segmentation and Clustering
Experimentation
NLP and Sentiment Analysis
Experience translating analytical findings into insights, recommendations, or measurable business outcomes for technical and non-technical stakeholders.
Comfort working in notebook-based analytical environments.
Strong curiosity and ability to learn new analytical methods and technologies quickly.
Excellent communication and data storytelling skills.
Master's degree in Data Science, Business Analytics, Statistics, Applied Mathematics, Economics, or another quantitative or analytical field.
Experience analyzing customer behavior, digital products, customer feedback, or customer experience.
Experience presenting complex analyses to executive audiences with decision-support materials.
Knowledge of financial services or digital banking products.
Advanced SQL and Python skills.
Experience with customer segmentation, behavioral analytics, product adoption, feature usage, and customer engagement.
Experience with Voice of the Customer (VoC), Voice of the Member (VoM), survey analytics, app feedback, and sentiment analysis.
Experience developing KPIs, dashboards, analytical reports, and executive presentations.
Relevant experience includes:
Customer Experience (CX) Analytics.
Customer Behavior Analytics.
Customer Segmentation and Behavioral Segmentation.
Customer Usage Analysis.
Customer Engagement.
Customer Journey Analytics.
Customer Friction and Pain-Point Analysis.
Product and Feature Adoption.
Digital Customer Behavior.
Digital Engagement and Usage Analytics.
Candidates should be able to analyze customer preferences, product choices, feature adoption, differences across customer segments, digital platform usage, and adoption drivers.
Relevant experience includes:
Mobile App Analytics.
Website Analytics.
Online Platform Analytics.
Digital Product Analytics.
User Behavior Analytics.
Click and Usage Behavior.
Digital Customer Journeys.
Feature Usage.
Product Adoption.
Digital Engagement.
Financial services and digital banking experience are beneficial but not mandatory when candidates have comparable digital product or customer analytics experience.
Relevant experience includes:
Voice of Customer (VoC).
Voice of Member (VoM).
Customer Feedback Analytics.
Survey Analytics.
App Feedback and App Reviews.
Customer Sentiment.
Social Media Feedback.
Digital Feedback Analysis.
Contact Center Interaction Analysis.
NLP and text analytics experience is valuable, particularly when applied to customer or member feedback.
Relevant skills include:
Natural Language Processing (NLP).
Text Analytics.
Sentiment Analysis.
Survey and App Review Analysis.
Social Media Analysis.
Topic and Theme Identification.
Text Classification.
Keyword Analysis.
Unstructured Text Analysis.
NLP is beneficial but is not a mandatory technical requirement ahead of SQL, Python, and strong exploratory/statistical analysis.
Candidates should demonstrate how they have used analytics and data science to solve business problems and influence decisions.
The expected approach is:
Business Problem → Analysis/Data Science → Insight → Recommendation/Decision → Business Outcome
Candidates should be able to explain:
Why an analysis or model was created.
What business problem it addressed.
What customer behavior was examined.
What the analysis uncovered.
What recommendations or decisions resulted.
Who used the findings.
Whether the work influenced a business or product decision.
Whether a measurable outcome was achieved.
Model development or improved model accuracy alone is not sufficient without a clear business application.
This is a stakeholder-facing and collaborative role.
Relevant experience includes:
Business Stakeholder Partnership.
Product Team Collaboration.
Cross-Functional Collaboration.
Translating Technical Findings.
Business Recommendations.
Data Storytelling.
Decision Support.
Executive Communication.
KPI Development and Monitoring.
Dashboard Development.
Executive Dashboards and Presentations.
Candidates should be able to interpret analytical results, communicate business implications, and recommend actions—not simply build dashboards or deliver technical outputs.
AI is used primarily to support analytical activities, including summarizing data, summarizing customer feedback, generating insights, and assisting analytical work.
The role is not primarily focused on:
AI Engineering.
Machine Learning Engineering.
MLOps.
Model Deployment.
AI Architecture.
Production ML Infrastructure.
These skills may be beneficial, but customer-focused applied analytics and business outcomes are the primary priorities.
Candidates do not need to have a current or previous title of Data Scientist. Relevant transferable backgrounds include:
Customer Analytics.
Product Analytics.
Digital Analytics.
Customer Insights.
Applied Research.
Decision Science.
Business Analytics.
Customer Success Analytics.
Marketing and Consumer Analytics.
The key requirement is demonstrated technical analytics, customer behavior analysis, and business application.
Candidates with approximately 1–5 years of experience are acceptable. Candidates with 1–2 years of experience and entry level candidates may also be considered when they demonstrate strong applied work through:
Master's or graduate projects.
Data science program projects.
Internships.
Early-career projects.
Applied research.
Customer behavior projects.
Real-world business problem solving.
A Master's degree in Data Science or a related quantitative field is valuable, particularly for candidates with fewer years of professional experience. Graduate projects are relevant when they demonstrate applied data science, customer behavior analysis, business application, and communication of results.
