Audit Innovation & Data Analytics Manager - Hybrid

Job Summary

Collaborates with the Innovation and Data Analytics Center of Excellence, the Audit Delivery teams, and lines of businesses to increase the analytic capabilities within Synovus Audit Services. Helps with the development and employment of quantitative and qualitative analysis in support of audits of Synovus' key strategic objectives, leveraging coding approaches from various software packages and querying tools. Typically leads multiple efforts in support of audit engagements and projects. Challenges the completeness and accuracy of data sources and interprets the outcome of analytics to reveal and effectively communicate insights.

Job Duties and Responsibilities

• Acts as a change agent within Synovus Audit. Develops, recommends and champions innovation and data analytic strategies and methodologies for use by Synovus Audit. Solid knowledge and understanding of audit or risk methodologies and techniques. Supports audits of Synovus processes which support key strategic objectives.
• Uses SQL to interact directly with database systems to assess data and generate queries for use within Audit, and in reporting, dashboarding and automation. Leverages coding approaches in SQL, Python, R, Power BI, or other analytic tools/programming languages to develop, test, evaluate, and implement meaningful insights. Uses data analysis techniques to acquire, transform, and translate large and complex datasets into data-driven solutions that identify trends, risks, outliers, and unexpected patterns. Analyzes data to identify and implement new data solutions that improve effectiveness of the audit plan and execution across our audit universe, and communicates the strengths and weaknesses of the various approaches within Audit. Provides input and develops recommendations to further audit assurance.
• Leads project management efforts for Data Analytics in support of audit engagements, including multiple projects across several stakeholders, users, and analysts. Coordinates with cross-functional teams to determine sources of data for assurance testing and evaluating the results. Collaborates with internal audit stakeholders, business units, and audit leadership to develop data-driven insights into business processes and controls. Iterates and refines business intelligence solutions as new information becomes available or as requirements are refined. Provides quantitative and qualitative reporting and analysis for data-driven assurance activities, and value-added actionable recommendations to improve audit engagement testing. Summarizes key findings and tells the story from the analysis. Presents and explains technical information in a way that establishes rapport, persuades others, and promotes understanding.
• Supports efforts to upskill and enable Audit team members in data analytics, including data mining, sourcing, cleaning, transferring, and loading to analytics tools. Coaches team members regarding technical coding skills and the use of automation in various phases of the audit lifecycle.
• Drives the innovative use of analytics through all phases of an audit. Identifies and recommends opportunities for alternative approaches or solutions to provide greater risk assurance. Remains current on trends in data analytics and data science, including techniques, tools, and best practices. Helps identify emerging trends in technology for future growth and development.
• Each team member is expected to be aware of risk within their functional area. This includes observing all policies, procedures, laws, regulations and risk limits specific to their role. Additionally, they should raise and report known or suspected violations to the appropriate Company authority in a timely fashion.
• Performs other related duties as required.

The information on this description has been designed to indicate the general nature and level of work performed by employees within this classification. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities, and qualifications required of employees assigned to this job.

Synovus is an Equal Opportunity Employer supporting diversity in the workplace

Minimum Education:

• Bachelor's degree in Computer Science, Analytics, Statistics, Mathematics or a related quantitative discipline and/or business or an equivalent combination of education and experience.

Minimum Experience:

• Five years of hands-on experience in analytics, reporting, and/or modeling; experience in application of data programming and technical experience in financial services and/or banking database technology.
• Required Knowledge, Skills, & Abilities:
• Specialized training and work experience with data analytics and/or data science
• Proficiency using Microsoft Office software products with advanced proficiency using Excel and Visual Basic. Proficiency in utilizing one or more analytic tool/programming language such as SQL, SAS, Python, R, or ACL
• Proficiency using SQL and extracting data from relational databases. Proficiency slicing and dicing data, and power analytical and reporting processes. Coding and understanding queries with minimal use of drag-and-drop tools
• Leadership aptitude and experience, and the ability to mentor junior team members
• Project Management experience and skills in managing multiple projects simultaneously
• Excellent verbal, written, and interpersonal communication skills. Professional technical writing skills for documenting analytical results and processes. Ability to present and explain technical information in a way that establishes rapport, persuades others, and promotes understanding to both technical and non-technical audiences. Ability to use data to tell a story visually and chronologically
• Be a critical thinker with an innovative mindset. Serve as a change agent challenging conventional thinking, approaches and methodology.
• Eager to learn new analytical techniques, systems, data, and techniques.

Preferred Knowledge, Skills, & Abilities:

• Exposure to advanced analytic techniques, including Machine Learning and Natural Language Processing
• Exposure to business intelligence or data visualization solutions, such as SAP Business Objects, Power BI, Tableau, or Qlikview/Qliksense