Title: Lead Data Scientist Location: Charlotte, NC / Detroit, MI About the Role We are seeking a highly skilled and experienced Lead Data Scientist to drive our customer journey analytics and customer 360 initiatives. This role requires a strong blend of data science, data engineering, and statistical expertise, with a primary focus on leveraging Python and AWS services. The ideal candidate will be passionate about uncovering deep customer insights, building robust data pipelines, and developing predictive models that enhance customer experience and business outcomes. Responsibilities: • Customer Journey Analytics: • Design, develop, and implement end-to-end customer journey analytics solutions, from data ingestion to actionable insights. • Utilize advanced statistical methods and machine learning techniques to map, analyze, and optimize customer touchpoints and pathways. • Identify key moments of truth, friction points, and opportunities for personalization within the customer journey. • Develop predictive models for customer churn, lifetime value (CLTV), next best action, and conversion probability across various stages of the customer journey. • Customer 360 Development: • Architect and build comprehensive Customer 360 platforms, integrating data from diverse sources (transactional, behavioral, demographic, social, etc.). • Lead efforts in data unification, identity resolution, and master data management to create a single, accurate view of the customer. • Ensure data quality, consistency, and accessibility for various analytical and operational use cases. • Data Science & Statistical Modeling: • Apply a wide range of data science techniques, including but not limited to: • Regression analysis (linear, logistic, etc.) • Classification (e.g., Random Forest, Gradient Boosting, SVM) • Clustering (e.g., K-Means, DBSCAN, Hierarchical Clustering) • Time Series Analysis • Natural Language Processing (NLP) for unstructured customer feedback • Causal inference and A/B testing analysis • Develop and validate statistical models, ensuring their accuracy, robustness, and interpretability. • Translate complex analytical findings into clear, concise, and actionable recommendations for business stakeholders. • Data Engineering & Pipeline Development: • Design, build, and maintain scalable and efficient data pipelines to extract, transform, and load (ETL/ELT) large volumes of customer data. • Leverage AWS services (e.g., S3, Glue, Lambda, EMR, Athena, Redshift) for data storage, processing, and orchestration. • Work with Snowflake for data warehousing and analytics, optimizing data models and queries for performance. • Integrate and manage data from MongoDB and other NoSQL databases for specific customer data points. Required Skills & Experience: • 5-7 years of progressive experience in data science, with a strong focus on customer analytics and building Customer 360 solutions. • Expert proficiency in Python for data manipulation, statistical modeling, and machine learning. • Relevant Python libraries: pandas, NumPy, scikit-learn, SciPy, Statsmodels, Matplotlib, Seaborn, Plotly, NetworkX (for graph-based journey analysis), FuzzyWuzzy (for identity matching), airflow (for workflow orchestration). • Extensive experience with AWS services for data engineering and machine learning (e.g., S3, Glue, Lambda, EMR, Athena, Redshift, SageMaker). • Strong hands-on experience with Snowflake for data warehousing, modeling, and performance optimization. • Proficiency with MongoDB for handling semi-structured or unstructured customer data. • Solid understanding of statistical concepts, hypothesis testing, experimental design, and causal inference. • Experience with SQL and data modeling for both relational and NoSQL databases. • Proven ability to translate complex data into clear, actionable business insights and communicate them effectively to non-technical audiences. • Experience with version control systems (e.g., Git). • Excellent problem-solving skills and a strong analytical mindset. Preferred Qualifications: • Experience with real-time data streaming and processing (e.g., Kafka, Kinesis). • Familiarity with other big data technologies (e.g., Apache Spark). • Experience with A/B testing frameworks and experimentation platforms. • Knowledge of customer relationship management (CRM) systems and marketing automation platforms. • Relevant experience within the digital banking or financial industry is preferred. Education • Bachelor's or Master's degree in Mathematics, Statistics, Computer Science, Data Science, Artificial Intelligence, or a similar quantitative field is required.