Execute end-to-end analytical workflows including data extraction, feature engineering, model development, validation, deployment, monitoring, and performance measurement.
Develop scalable and reusable analytical solutions, model frameworks, and code libraries.
Evaluate and implement emerging analytical methodologies and technologies to improve business outcomes.
Analyze customer acquisition, onboarding, engagement, retention, funding behavior, and product performance.
Develop customer segmentation methodologies and behavioral models to support growth initiatives and customer experience improvements.
Identify key drivers of customer value, retention, attrition, profitability, and portfolio performance.
Conduct deep-dive analyses to uncover actionable insights and business opportunities.
Quantify the impact of business initiatives using statistical and analytical methodologies.
Develop forecasting models for customer growth, balances, deposits, funding activity, and other key business metrics.
Build optimization frameworks to support business planning, customer strategies, portfolio management, and pricing decisions.
Evaluate scenarios and quantify business risks, opportunities, and expected outcomes.
Partner with stakeholders to define performance metrics and measure business results.
Design, execute, and evaluate controlled experiments, A/B tests, and pilot programs.
Establish measurement methodologies and success metrics for strategic initiatives.
Apply causal inference and statistical techniques to measure incremental business impact.
Provide recommendations based on quantitative evaluation of business strategies and initiatives.
Develop and optimize SQL and Python solutions for data extraction, transformation, analysis, and automation.
Build scalable analytical datasets, automated workflows, and data pipelines.
Develop reusable tools and frameworks to improve efficiency and consistency across analytical projects.
Partner with Technology and Data Engineering teams to improve data accessibility, reliability, and scalability.
Investigate data anomalies, reporting discrepancies, and model performance issues.
Perform root-cause analysis and recommend corrective actions.
Develop automated data quality controls, validation processes, and monitoring frameworks.
Ensure analytical solutions adhere to governance, documentation, and reproducibility standards.
Partner with business leaders and cross-functional teams to define analytical requirements and prioritize opportunities.
Translate business challenges into analytical approaches and technical solutions.
Present analytical findings, model results, assumptions, risks, and recommendations to technical and non-technical audiences.
Serve as a trusted advisor on advanced analytics, machine learning, and measurement methodologies.
Experience Requirements
3+ years in Data Science, Machine Learning, Advanced Analytics, Quantitative Modeling, or a related field.
Experience delivering end-to-end analytical solutions from requirements through implementation and production support.
Experience with large-scale structured and unstructured datasets.
Experience partnering with stakeholders to solve business problems with data-driven solutions.
Advanced SQL for large-scale data extraction, transformation, and analysis.
Python for analytics, automation, machine learning, and model development.
Statistical modeling and machine learning techniques, predictive analytics, and forecasting.
Experimental design, hypothesis testing, and causal inference.
Data visualization and data storytelling.
Data quality validation and root-cause analysis.
Large-scale data processing and analytics.
Strong verbal and written communication with the ability to translate analytical findings into actionable recommendations.
Education Requirements
Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Economics, Engineering, Operations Research, Physics, or a related quantitative field required.
Master's degree or higher in a related quantitative discipline preferred.
