Data Scientist I

Bank of America•Jersey City, NJ

21h•$115,000 - $143,000•Onsite

About The Position

At Bank of America, the Data Scientist I analyzes and interprets large datasets to identify potential revenue opportunities and develop risk management strategies. The role collaborates with stakeholders to understand business problems, applies data-gathering and analytical methods to develop solutions, and presents recommendations based on findings. It requires a disciplined, accountable, resilient, and flexible approach, supporting responsible growth for the enterprise. The Data Scientist develops analytical solutions for credit risk, fraud analytics, customer segmentation, and portfolio management. The role extracts, transforms, and analyzes data from enterprise platforms; builds dashboards, reports, and automated analytical workflows; and supports credit risk scorecard development, monitoring, performance tracking, and stress testing. Additional work includes validating data quality, reconciling information, improving processes, conducting portfolio and trend analysis, forecasting losses, and supplementing company data with third-party sources when needed. The position performs business analytics using methods such as machine learning, text mining, and statistical analysis; applies agile practices to project management, solution development, deployment, and maintenance; and documents business requirements and technical specifications for analytical solutions and their production implementation. The Data Scientist manages multiple priorities while delivering quality work on time, communicates technical concepts and results to internal stakeholders, builds collaborative professional relationships, identifies potential issues and controls to mitigate risk, and researches advances in data science and artificial intelligence. This role is based at 525 Washington Blvd, Jersey City, New Jersey, and is part of Bank of America’s in-office culture, with role-specific flexibility based on responsibilities and business needs. It is a first-shift position with a 40-hour workweek. The annual salary range is $115,000 to $143,000, with offers based on experience, education, and skill set. The position is eligible for an annual discretionary incentive plan and benefits, including paid time off.

Requirements

Master's degree or higher in Computer Science, Data Science, Statistics, Mathematics, Finance, or a related quantitative discipline.
Strong STEM background and knowledge of machine learning, natural language processing, deep learning, statistical modeling, quantitative analysis, forecasting, and data visualization.
At least 2 years of experience developing Tableau data visualizations, reports, and dashboards.
Proficiency in programming languages and tools such as Python (including scikit-learn, pandas, and NumPy), SQL, SAS, R, MATLAB, or C/C++.
Prior experience with SQL and NoSQL databases and distributed computing.
Applied statistics skills, including distributions, statistical testing, and regression.
Strong business analysis and documentation skills, including requirements, process flows, and data lineage.
Ability to manage multiple and competing priorities.
Excellent written and verbal communication skills.

Responsibilities

Analyze large datasets to uncover revenue-generation opportunities and develop risk-management strategies.
Collaborate with stakeholders to understand business problems, develop analytical solutions, and present recommendations.
Perform business analytics, including data analysis, trend identification, and pattern recognition using machine learning, text mining, statistical analysis, and other advanced techniques.
Develop analytical solutions for credit risk, fraud analytics, customer segmentation, and portfolio management.
Extract, transform, and analyze large datasets from enterprise data platforms.
Build dashboards, reports, and automated analytical workflows and solutions.
Support credit risk scorecard development, monitoring, performance tracking, and stress testing.
Perform data quality validation, reconciliation, and process improvement initiatives.
Conduct portfolio analytics, segmentation, trend analysis, and loss forecasting.
Extend company data with third-party information sources when needed.
Apply agile practices to project management, solution development, deployment, and maintenance.
Create and maintain technical documentation of business requirements and specifications for analytical solutions and their production implementation.
Manage multiple priorities and deliver quality, timely quantitative models, data science products, analysis reports, and visualizations, independently and as part of a team.
Communicate technical concepts and analysis results to internal stakeholders through presentations and in-person and virtual discussions, and build professional relationships to foster collaboration.
Identify potential issues and develop controls to mitigate risk.
Research advances in data science and artificial intelligence to support business analytics.