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“Data Scientist” has been ranked among the top three jobs in America since 2016. It even held the top spot up until 2020. The US Bureau of Labor Statistics estimates a 28% increase in data science job openings through 2026 — but should your PE or investment banking firm consider hiring for this popular and highly specialized role?
Taking a look at our own research, it’s not hard to see why data scientists are in such high demand. Dealmakers that leverage data and technology transact 3.5x more frequently than their peers. They also generate IRR 8.8 percentage points higher in comparison.
Yet despite the clear impact sophisticated data and technology use have on modern dealmakers, data scientists are still a novelty in private equity and investment banking, which have been slower to undergo digital transformation compared to other industries. In a recent webinar hosted by Private Equity Insights, the audience ranked most dealmakers’ digitization
progress a 2 out of 5, with 5 being fully transformed. The majority of firms (67%) believe that the greatest impact of artificial intelligence (AI) is yet to come within the next five years.
However, today’s highly competitive and volatile landscape is leading a growing number of firms to embrace data to help them find and close more deals faster. A recent survey shows that data analytics has become more important for 70% of PE firms in the last twelve months. In addition, more than ¾ of dealmakers have either implemented or are in the process of evaluating cloud technologies.
As dealmakers continue down the path of digital transformation, the technology they need to implement will grow progressively complex. The level of data insights they wish to uncover will get deeper and increasingly proprietary. And the people they need to hire to achieve these goals will become more specialized.
We recently sat down with Tom Lesnick, CEO and Founder of Copilot, and John Lucas, SourceScrub’s own Data Scientist, to discuss what makes data scientists such pivotal hires for dealmakers. Read on to learn what exactly these experts bring to the table, signs it’s time to search for the right candidate, what skills and experience to screen for, and much more.
It’s critical to understand the value of data scientists and what makes them different from other data professionals before you can determine whether it’s time to hire one. Although their titles are often used interchangeably, data analysts and data scientists have different skillsets and responsibilities.
Data analysts are spreadsheet gurus capable of wrangling large datasets and finding the proverbial needle in the haystack. They’re able to find answers to most foundational data questions, create dashboards and reports, and standardize meaningful KPIs. They usually work with existing, structured data from primary and secondary sources to measure past performance and identify high-level trends.
Data scientists are usually hired after data analysts once firms are ready to take these capabilities to the next level. Rather than focusing on existing, clearly defined datasets, they often integrate both structured and unstructured data from across multiple sources to create new, custom views of the business.
They typically have a master’s degree or PhD in computer science or mathematics, and are able to develop various models and algorithms that map sophisticated data patterns, automate processes and analyses, and predict future business outcomes. Data scientists are well-versed in artificial intelligence (AI) and machine learning applications.
The more technology your firm uses, the more data you’ll generate. For example, many firms are turning to data service providers to surface advanced data on investment opportunities, from industry classification to ownership type to hiring trends. While reviewing a .CSV export or two is one thing, configuring different types of data into a single, multi-dimensional view that can be analyzed and understood holistically is another.
“The more data you have, the greater the potential of uncovering detailed and differentiated insights,” says Copilot’s Tom Lesnick, “but also the bigger problems like entity matching and data resolution become. Analysis becomes less of a simple SQL issue and more of a modeling or fuzzy matching challenge that must tackled by data scientists. There’s also the opportunity to create algorithms that can automate a lot of this work.”
Pro Tip: The Importance of Data Infrastructure
Before a data scientist can begin integrating, analyzing, and modeling data, it’s important for an organization to have the appropriate infrastructure in place. “Managing today’s volume and variety of data simply cannot be done on local machines,” says SourceScrub’s John Lucas. “It must be done in the cloud using platforms like Databricks (Apache Spark).”
While most data scientists have experience implementing these types of technologies, Tom recommends hiring a Senior Data Engineer to set up and own this infrastructure prior to bringing a data scientist on board. “Without this person the data scientist will spend a lot of time gathering and massaging data,” he says. “They’re much happier and their time is much better spent focusing on the statistical modeling and mathematical side of the house.
PE and investment banking firms that are just starting to capture and use data must walk before they can run. Leveraging the high-level dashboards and pre-built reports that come standard with most business applications is a good place to start. A clear understanding of past performance is necessary to prepare for the future.
However, dealmakers that are ready to hire a data scientist have grown to understand that the true value of data lies in its ability to yield proprietary insights that create true market advantage. They’re ready to develop a unique point of view that empowers them to not only identify promising new opportunities earlier than the competition, but to also know when existing targets are exhibiting subtle signs of growth and investment readiness.
“We’re not talking about basic regression models here,” says Tom. “True data science surfaces actionable patterns and predictable outcomes by extracting data signals from across multiple, disparate sources that are not so easily correlated together. It goes beyond standardized reports and isolated insights to generate algorithms, machine learning models, insight frameworks, and data assets that can transform a firm’s entire investment strategy.”
For firms with a high volume of raw data looking for a few fast insights to help guide their investment theses, a data science consultant may be the best and most cost-effective route. But for dealmakers with the intention of completing digital transformation and building a data-centric organization, hiring a full-time data scientist is the right move.
John cautions firms against looking for a “quick fix” by simply buying technology that promises AI and machine learning functionality. “It’s not magic, and there isn’t generally available AI that will solve everyone’s problems,” he says. “Data is error-prone, and eventually everything has to be supervised to ensure data quality and accuracy.” Firms need to be ready to invest in human capital — as well as data platforms and services that involve knowledge workers in the data scrubbing process — to ensure all insights are accurate and in context.
Tom advises all of Copilot’s customers to approach their data science endeavors as if they’re building a new product. “It’s a serious undertaking. You have to have strong buy-in from senior members of the deal team and build a culture that sees the value, understands the challenges, and is willing to put in the effort,” he shares.
Once you’ve determined that your firm is ready to hire a data scientist, it’s time to start sourcing resumes! But since data science is still a new function for most private equity and investment banking firms, finding a candidate that comes from the dealmaking space will likely prove to be difficult.
John suggests sourcing candidates from spaces that solve data challenges similar to dealmakers’ needs. These include working with exceptionally large datasets, integrating data from across dozens of dissimilar sources, and searching for answers that aren’t immediately obvious. “Data scientists that come from the healthcare and tech industries are often good fits,” reveals John.
Most of the tools and skills data scientists will have used during their time at companies in other industries are also relevant to dealmakers. Some key skills and technologies to scan resumes and LinkedIn profiles for include:
Most importantly, a good data scientist will be more than a data-cruncher — they will be deeply interested in and capable of understanding not only the dealmaking industry, but also the unique challenges and opportunities of your specific firm. “If a candidate can’t get their head around the nuances or thesis of the firm, then it’s going to be hard for them to translate that into something meaningful,” reveals Tom. “There is a lot of iteration and testing that happens around various investment hypotheses, and they really need to understand what they’re trying to solve for and
why it’s needed.”
He continues: “The person who will excel in this role will be passionate about your firm and find it genuinely interesting. Using data to find the next Snowflake or AirBnB is a pretty cool opportunity, and you want to find people who are excited about it.”
Here are just a few examples of different ways leading dealmakers are using data science to set themselves and their portfolio companies apart from the competition. “At Copilot we were working with a firm that had a statistical regression model in place to help them classify and prioritize investment targets as A companies, B companies, and so forth,” shares Tom.
Unfortunately, the model was only about 80% accurate, causing the sourcing team to lose faith in their data. “We were able to help the firm replace that regression model with something more sophisticated that greatly reduced the number of false positives and gave the deal team confidence in the outputs” Tom says. “There’s a huge benefit of taking what you think you know about your data and investment thesis and using more advanced data science to take it to the
In another instance, TA Associates developed an algorithm that analyzed the clients of its portfolio companies in the B2B tech space over a three-year period. During that time, the algorithm learned how to identify which clients are most likely to grow their accounts with 96% accuracy. Sharing this information with their portfolio companies has been a major value add for TA Associates.
Finally, a leading private equity group sought to better pinpoint opportunities that aligned with its portfolio companies’ add-on strategies. To generate exclusive market intelligence, the firm integrated SourceScrub’s full private company dataset with information from key internal sources in its data warehouse. The team now runs complex analyses and develops custom models to surface previously hidden opportunities that match portfolio companies’ unique corporate development criteria. The result? A 3x increase in deal volume!
Hiring a data scientist is an exciting and pivotal step for private equity and investment banking firms. The person in this role is capable of unlocking unforeseen insights with the potential to empower your firm to leapfrog the competition.
But making sure your firm has the right goals, infrastructure, and expectations before bringing a
data scientist on board is crucial to their success. Start by asking yourself these questions:
If you answered yes to most of these questions, it’s time to hire a data scientist! If not, don’t worry — your time will come. Either way, learning more about how your firm can successfully complete digital transformation and make better use of data and technology is a positive step.
Download our free guide for modern dealmakers: Why 78% of Digital Transformation Initiatives Fail and What to Do About It.