Cons Prod Strategic Analyst IV - Fraud Data Science Modeling

Bank of America-posted about 1 year ago

Full-time • Mid Level

Plano, TX

Credit Intermediation and Related Activities

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The Cons Prod Strategic Analyst IV - Fraud Data Science Modeling role at Bank of America focuses on performing complex analyses to enhance portfolio risk, profitability, and operational performance for consumer products, particularly in fraud prevention and detection. The position requires collaboration with various teams to deploy advanced analytical solutions aimed at reducing fraud losses and improving client experience.

Performs complex analysis of financial models, market data, financial data, and portfolio trends to understand product performance and improve portfolio risk, profitability, performance forecasting, and operational performance.
Coaches and mentors peers to improve proficiency in a variety of systems and serves as a subject matter expert on multiple business and technical-related topics.
Identifies business trends based on economic and portfolio conditions and communicates findings to senior management.
Supports execution of large scale projects, such as platform conversions or new project integrations by conducting advanced reporting and drawing analytics based insights.
Link Analysis/Graph analytics to find and mitigate densely connected fraud networks.
Developing and tuning graph algorithms to maximize detection of fraud.
Assist with the generation, prioritization, and investigation of fraud rings.

A minimum of 4 years of experience in data and analytics is required.
Must be proficient with SQL and one of SAS, Python, or Java.
Critical problem-solving skills including selection of data and deployment of solutions.
Proven ability to manage projects, exercise thought leadership and work with limited direction on complex problems to achieve project goals while also leading a broader team.
Excellent communication and influencing skills.
Thrives in fast-paced and highly dynamic environment.
Intellectual curiosity and strong urge to figure out the 'whys' of a problem and come up with creative solutions.
Expertise handling data across its lifecycle in a variety of formats and storage technologies (e.g., structured, semi-structured, unstructured; graph; hadoop; kafka).
Expertise in data analytics and technical development lifecycles including having coached junior staff.

Advanced Quantitative degree (Masters or PhD).
7+ years of experience; work in financial services is very helpful, with preference to fraud, credit, cybersecurity, or other heavily quantitative areas.
Understanding of advanced machine learning methodologies including neural networks, ensemble learning like XGB, and other techniques.
Proficient with H2O or similar advanced analytical tools.

Competitive benefits to support physical, emotional, and financial well-being.

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