Decision Scientist II

Truist•Atlanta, GA

2d•Hybrid

About The Position

Act as an individual contributor supporting analytics projects and executing against the objectives of assigned business group. Using an interdisciplinary approach of leveraging concepts from business, applied statistics and math, operations research, information technology, process design and behavioral sciences, will work both independently and with internal teammates to produce analytic insights that help the Line of Business (LOB) make informed, data-driven decisions with an objective of driving quantifiable, optimized business results in support of company goals. Focus on high impact, visible analyses and initiatives across multiple business models, covering banking channels, segments, and products. Partner on target initiatives as assigned; work independently and with internal teammates to drive decision science projects leveraging quantitative analysis techniques, including machine learning, in pursuit of business optimization and impact. Pursue business outcomes valued through increased revenue and/or efficiency leveraging data-driven insights powered by analytics in support of enhanced decision-making. Focus on continuous improvement in decision science delivery and outcomes in pursuit of business optimization. Explore and apply tools to solve business challenges and deliver solutions that are timely, accurate, and repeatable. Exercise sound judgment, risk management, and foster a client centric culture throughout design, development, and deployment practices. Foster communication and partnership across multiple levels of the organization including engagement with LOB contributors and junior-level managers.

Requirements

Bachelor’s degree in Data Science, Analytics, Statistics, Finance or related quantitative field
3 years of experience in analytics or research positions performing/utilizing the following:
Performing quantitative analysis and data analytics
Statistical methods, including a broad understanding of classical statistics, probability theory, econometrics, time-series, and primary statistical tests
Data cleansing and preparation methodologies, including regex, filtering, indexing, interpolation, and outlier treatment.
Data Analysis techniques, EDA, Data Visualization to effectively communicate to stakeholders, clarify requirements and make effective suggestions.
Data Engineering and ML-Ops to both effectively extract, transform, load the data and for further model deployment, including model maintenance and operation.
Working knowledge of transaction processing application software, application processing systems, and collection inventory systems
Performing in a cross-functional and collaborative team environment focused on supporting business partners with enhanced insights
Connecting to practical application, providing recommendations on target initiatives, with a strong bias to action and focus on quantifiable business impact
Delivering insights against tight deadlines in a collaborative environment to drive maximum impact
Applying knowledge in statistical and analytical principles, tools, and techniques
Natural Language Processing techniques and other related Deep Learning knowledge.
Utilizing knowledge and experience with: IBM DB2 and Oracle, SAS, SQL, Toad, R, Python, SAS E-Miner, Data visualization and BI tools including Tableau and MicroStrategy, and Proficiency in Microsoft Office Suite (Excel, PowerPoint, Word)

Responsibilities

Act as an individual contributor supporting analytics projects and executing against the objectives of assigned business group.
Produce analytic insights that help the Line of Business (LOB) make informed, data-driven decisions with an objective of driving quantifiable, optimized business results in support of company goals.
Focus on high impact, visible analyses and initiatives across multiple business models, covering banking channels, segments, and products.
Drive decision science projects leveraging quantitative analysis techniques, including machine learning, in pursuit of business optimization and impact.
Pursue business outcomes valued through increased revenue and/or efficiency leveraging data-driven insights powered by analytics in support of enhanced decision-making.
Focus on continuous improvement in decision science delivery and outcomes in pursuit of business optimization.
Explore and apply tools to solve business challenges and deliver solutions that are timely, accurate, and repeatable.
Exercise sound judgment, risk management, and foster a client centric culture throughout design, development, and deployment practices.
Foster communication and partnership across multiple levels of the organization including engagement with LOB contributors and junior-level managers.