Data scientist at kpmg

Data scientist at kpmg DEFAULT

Audit Data & Analytics careers

Audit Data & Analytics (D&A) has three main areas of focus. These are:

  • enhanced audit quality
  • client insights
  • automation.

Sitting within the broader Audit division, we provide specialist data support for audit teams from planning through to execution.

We start by planning and designing D&A procedures for the audit. We extract data from a variety of client systems and we clean and sanitise large quantities of data. Following that, we analyse the data to identify risks and insights for the audit. We then visualise data so it’s in an easy to understand format then present findings to audit team and client management.

We’re all interested in the latest trends in technology and data. We have a strong focus on innovation through the testing and development of the new technologies including Microsoft Power BI, machine learning, blockchain technology, augmented reality and automation.

Our multi-disciplinary team has a depth of expertise in statistics, computer science and data analysis. There is a range of nationalities and degrees.


Practice Area

KPMG Careers

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While many PE firms recognize they need to leverage data better, few understand what they need to do in order to implement a data science approach to support their investments. A fully integrated data science approach will include the right combination of people, processes, technology and tools, and data sources. The question to consider is whether it makes the most sense to build these capabilities in-house or to partner with an external firm that specializes in applying data science.

Decision-making criteria

Making a decision to build or buy data science capabilities is an important strategic direction that requires careful consideration. In thinking about the right approach, these criteria should be taken into account.

Business complexity

Complexity is a leading determinant of whether a company can successfully scale up an in-house data science capability. Evaluate the number of portfolio companies, industry concentration, annual deal volume, and other factors. In the face of high complexity, companies should consider partnering with an external firm.

Willingness to invest

A data science capability requires a wide range of tools, resources, time, and investment. A PE firm needs to evaluate their willingness to invest in enterprise-level solutions and continuously invest to keep up-to-date on skills, tools, and data. Evaluate the percentage of revenue that the PE firm is willing to commit to growing the capability, and the strategic relevance of data science moving forward.

Current capabilities

PE firms need to evaluate the size of the “gap” between their desired end-state and their existing talent and tools. Moving from an existing in-house advanced analytics capability to data science and machine learning is a smaller gap than starting from scratch. Evaluate the percentage of deals using D&A to inform decisions, people, processes and existing tools and technology.

Bottom line

If a PE firm evaluates its business strategy as “Build” across all three criteria, then developing a full inhouse data science capability is a feasible option to consider. If the firm evaluates their business as “Buy” across any of these, then partnering with an experienced data science team (“buy”) is the recommended approach.

Taking the next steps

Once a decision has been made to partner with an outside firm or to build a full in-house capability, PE firms should quickly take the necessary steps to integrate data science into their processes.

View the Global report or visit the KPMG US website to read the original publication.


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