Data Scientist

Key Skills

Java Programming, Python Programming, Database, SQL, R Programming

Job Description

Reporting to the Data & Analytics Manager, the Data Scientist is accountable for providing data analytics to Truist Wealth, across the full suite of products and client experience. While working with Sr. Data Scientist and the Data Scientist Team Leader the primary focus areas include portfolio, scenario, and strategic analysis, and ad-hoc Executive analytics. Other focus areas include marketing campaign response, evaluating success of strategic initiatives, account acquisition and management, delinquency and default, overall portfolio asset quality, and portfolio acquisitions and divestitures.

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time.

• Translate business opportunities into data science problems by defining project scope and performance metrics to measure success
• Perform sophisticated data analytics (encompassing data mining, inferential statistical analysis, and predictive analytics, for example) utilizing internal and external data
• Work with Sr. Data Scientist and Data Scientist Team Leader to identify actionable insights that measurably improve business outcomes or reduces business risk through complex analysis that results in successfully presenting findings to key stakeholder
• Work with Sr. Data Scientist and Data Scientist Team Leader to story tell with data by creating clear, concise, and relevant presentations that leverage data visualization tools to highlight and communicate opportunity to business stakeholders
• Collect and prepare data for reporting, analysis, and ad hoc request which encompasses exploratory to advanced predictive and/or modeling analytics to identify data relationships, patterns, and trends
• Collaborate and partner with teammates in Wealth Data and Analytics, EDO, Technology, and other business specific D&A teams to build best in class approaches to solve business and analytical problems, share best practices, and cross pollinate data knowledge

The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Bachelor’s Degree in a quantitative field related Mathematics, Statistics, Econometrics, Actuarial Science, Computer science, Engineering, Finance or a Bachelor’s degree with accredited data & analytics certifications and/or demonstrated commitment to the data and analytics career path through coursework
• 3-5 years of relevant financial services, data & analytics experience, and experience developing statistics and machine learning models
• Strong verbal and written communication skills. Ability to communicate technical details at a level appropriate for the audience
• Attention to detail and a sense of ownership for deliverables
• Ability to perform and deliver under very demanding and/or ambiguous situations
• Moderate reporting and automation experience leveraging BI and analytics applications
• Demonstrated problem solving skills and proven ability to perform and deliver under very demanding and/or ambiguous situations Demonstrated technical proficiency in one or more of the following languages SAS (base, enterprise guide or enterprise miner) and/or R/Python while working on projects to Probability and Statistics, Finance (financial ratios, interest rates, yield curve), or data mining
• Hands-on experience working with large databases and deep understanding of one or many of the following: Decision trees, Linear and Nonlinear regressions, neural nets, ensembles, Market Basket analysis, Time series forecasting, deep learning, text mining and various model assessment & performance metrics

Preferred Qualifications:

Master’s degree in business and/or a quantitative field related Mathematics, Statistics, Econometrics, Actuarial Science, Computer science, or Engineering Experience with big data technology (Hadoop, Hive, Spark, etc.) and data processing pipeline. Back ground within Wealth Management