Customer Analytics Data Scientist

Job Description

We are seeking a highly analytical and results-driven Customer Analytics Data Scientist to join our growing Data Science team. In this role, you will leverage advanced analytics, machine learning, and statistical modeling to understand customer behavior, improve customer experiences, and drive strategic business decisions. You will work closely with Product, Marketing, Sales, Customer Success, and Engineering teams to transform large datasets into actionable insights that enhance customer acquisition, engagement, retention, and lifetime value.

The ideal candidate has strong expertise in predictive analytics, customer segmentation, recommendation systems, experimentation, and data visualization. You should be proficient in Python, SQL, machine learning, and cloud-based analytics platforms, with the ability to communicate complex analytical findings to both technical and non-technical stakeholders.

Key Responsibilities

• Analyze large-scale customer data to identify trends, behaviors, and business opportunities.
• Develop predictive models for customer acquisition, retention, churn prediction, and lifetime value (CLV).
• Build customer segmentation models using clustering and behavioral analytics techniques.
• Design, execute, and evaluate A/B tests and multivariate experiments to optimize customer experiences.
• Develop recommendation systems and personalization models to improve customer engagement.
• Create forecasting models for customer demand, revenue, and marketing performance.
• Collaborate with Product, Marketing, Sales, and Customer Success teams to support data-driven decision-making.
• Design interactive dashboards and reports using business intelligence tools.
• Build and maintain scalable data pipelines for analytics and machine learning workflows.
• Apply statistical analysis and hypothesis testing to validate business initiatives.
• Monitor model performance, accuracy, and business impact, making improvements as needed.
• Ensure data quality, governance, privacy, and compliance with organizational policies.
• Present analytical findings and strategic recommendations to business leaders and executive stakeholders.
• Stay current with emerging technologies, machine learning techniques, and customer analytics best practices.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Economics, Information Systems, or a related field.
• 3–5+ years of experience in data science, customer analytics, business analytics, or predictive modeling.
• Strong programming experience with Python and SQL.
• Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Solid understanding of statistics, predictive modeling, clustering, classification, regression, and time-series analysis.
• Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Familiarity with ETL processes, data warehousing, and big data technologies.
• Excellent analytical thinking, problem-solving, and communication skills.
• Ability to explain technical concepts and analytical insights to non-technical audiences.

Preferred Qualifications

• Master’s degree in Data Science, Statistics, Computer Science, Business Analytics, or a related field.
• Experience with customer relationship management (CRM) analytics.
• Knowledge of recommendation engines and personalization algorithms.
• Experience with marketing analytics and digital customer journey analysis.
• Familiarity with MLOps tools and model deployment pipelines.
• Experience working in e-commerce, SaaS, fintech, retail, healthcare, or consumer technology industries.

Preferred Technical Skills

• Python
• SQL
• R
• Pandas
• NumPy
• Scikit-learn
• TensorFlow
• PyTorch
• Apache Spark
• Databricks
• Snowflake
• Tableau
• Power BI
• Looker
• Google Analytics 4 (GA4)
• Customer Data Platforms (CDPs)
• CRM Analytics
• Machine Learning
• Predictive Analytics
• Customer Segmentation
• Churn Prediction
• Customer Lifetime Value (CLV)
• Recommendation Systems
• A/B Testing
• Statistical Analysis
• Data Visualization
• Feature Engineering
• AWS
• Microsoft Azure
• Google Cloud Platform (GCP)
• Git
• Docker