The Internal Audit (IA) Data Analytics Manager will lead the department’s data analytics initiatives, which include developing and expanding the use of analytics across the internal processes (e.g. risk assessments, audit execution, etc.). As part of this role the IA Data Analytics Manager will lead a team of two, located in the same office and report to the Internal Audit Sr. Manager.
The data analytics scope covers key financial and operational processes and is also aimed at helping the business adopt similar capabilities to drive improved risk management and decision making.
- Manages the planning and execution of IA data analytics projects, including, but not limited to risk assessments, audit execution, and other special projects;
- Partners with other IA team members to define data analytics objectives across financial, operational, and IT audits;
- Focuses on developing clear and concise analytical approach to identify financial and operational risks and controls for the business and Internal Audit;
- Rapidly implements proofs of concepts for new candidate analytic approaches;
- Coaches and mentors data analytics staff to drive continuous improvement;
- Cultivates strong relationships across the organization, particularly within the IT organization.
- Bachelor’s degree in statistics, mathematics, computer science, or other related field and 6+ years professional experience, Advanced degree desired;
- Highly proficient in using data analytics and business intelligence tools such as SAS, R, R Studio, SQL, ACL,
- Cognos, Qlikview, Tableau, Toad, SAP ECC, SAP BW, SAP Lumira, and MS Office products;
- Proficient in statistical programming and modeling in SAS and/or other statistical software; working with real-life large unstructured dataset for data analysis
- Experience in helping define business problems or objectives and successfully designing and executing analytics solutions;
- Experience in leading teams; project management, relationship management, SOX, and/or internal auditing;
- Ability to utilize techniques such as multivariate regressions, Bayesian probabilities, clustering algorithms, machine learning, dynamic programming, stochastic-processes, queueing theory, algorithmic knowledge to efficiently research and solve complex problems.
- CIA, CISA, CPA, or industry recognized business intelligence /data science certification is a plus
- Highly organized, energetic, self-driven, and self-motivated;
- Strong written and verbal communication skills