Data scientist + platform professional
In 2012 Tom Davenport and D.J. Patil published an article in the Harvard Business Review titled “Data Scientist: The Sexiest Job of the 21st Century.”1 This article captured the growing enthusiasm for big data and the potential to reveal new business insights and profits. The authors argued that to realize the big data opportunity, companies needed to hire specialized talent— high-ranking professionals with the technical skills to build machine learning models, uncover unique patterns, design sophisticated dashboards, and reveal novel inferences. Their article also offered pointers on how to find and secure this new talent.
Nearly a decade later, the landscape is changing. The demand for strong data analytic capabilities remains. However, the requirements for success are shifting. A growing number of companies are seeking data science talent coupled with platform expertise. These roles seek data managers that can provide not only technical expertise on the model building but leadership on 1) data platform architecture and design; 2) systems to track and advise on emerging data technologies; 3) data standards and protocols; 4) best practices for managing data platform and solutions, and 5) the ability to create road maps to evolve the data platform to maximize business impact and minimize business risk. In short, they seek a blend of data science and platform acumen.
Take the example of Generate Biomedicines, a company backed by Flagship Pioneering, a privately-held biotechnology company based in Cambridge, Massachusetts that aims to reinvent the drug discovery process. The company is currently seeking a Director, Platform and Data Strategy. The position calls for “an analytically rigorous and technologically curious Platform Strategist to join the team. You will partner with scientific and business leaders across the company to drive platform data strategy for continual learning within and across modalities, technology evaluation and benchmarking, and overall value creation.”
The specific responsibilities of the position include the ability to:
· Build and drive iterative refinement of platform and modality roadmaps/strategy to maximize the impact of Generate’s machine learning platform · Help build and implement platform benchmarks and success metrics for continual monitoring of improvement across the entire platform from computational protein generation to candidate nomination · Support data tracking, analysis, and modeling of operations to enable informed decision making across talent and resources · Collaborate with scientific and business leadership to uncover and assess novel technologies or processes to expand platform capabilities and relieve bottlenecks before they arise –through both internal build and business development activity · Partner with machine learning and biology teams to drive data strategy to allow Generate to learn faster and observe exponential increases in technology capabilities
Candidates for this position are expected to not only have an advanced degree in quantitative sciences, applied math with experience in biotech or related fields, but also significant experience (5+ years) in relevant strategy, biotechnology platform building, venture creation, or top-tier management consulting firm.
Another current example is a position in London posted by Pleo, for a Director of Data Platform & Analytics. Pleo is a Danish fintech platform that offers smart company cards that enable employees to buy the things they need for work while keeping a company's finance department in control of spending. Since it was founded in 2015, it has built up a client base of over 15,000 companies.
The position for Director of Data Platform & Analytics is expected to:
· Drive the roadmap and direction of a Data Infrastructure Platform, which helps a variety of teams integrate and process their data · Define an ownership model and drive a strong culture of data quality across the company · Set up governance and security standards to ensure the right data gets in front of the right people at the company · Drive overall data literacy and culture in which data is used to generate new business value across all parts of the business. Advocate a culture of experimentation, backed up with data, and evangelize the organization about how data should be used day-to-day · Scale-up and define the structure of how data analytics & data science teams work across the organization to support decision-making in each department · Start a Data Science team and set them up for success to help various product & commercial teams to develop new data-driven experiences across Pleo's products · Directly manage multiple analytics & data science teams and indirectly work with functions that will be key partners to execute your strategy (operations, engineering, product) · Become a strong voice in the management and execution of the data strategy and putting it into practice across the company
Demand for these blended positions that combine data science expertise with platform expertise offers another career opportunity for platform professionals. This is a pragmatic shift from the initial captivation with the purely technical elements of data analytics.
A recent job description posted by Glassdoor for a Data Platform Architect captures this shift:
“We depend on our rich data to make business decisions, meaning it is a critical part of our business strategy. Our data teams are building a modern platform to power machine learning and sophisticated analytics processing for strategic business decision-making capability. Talk to us about this high-impact role if you would like to drive our data platform roadmap and partner with data engineering leaders to build a foundation that enables us to democratize access to data and empower decision making and data products.”
The reasons behind the growing demand for these blended capabilities are many but arise, in part, from previous failures. Many companies found it difficult to generate value from data scientists. Impressive qualifications and elaborate models often did not yield meaningful business outcomes. A study by Gartner in 2017 found that less than 20% of data science projects were completed and only about 8% of the ones that were generated significant value.2 While this assessment appears to be particularly stark, this and other postmortems3 have prompted companies to rethink how data analytics can best support the broader organization and competitive positioning.
As more companies reflect and learn from their previous hires demand for blended positions of analytics plus platform expertise and experience will continue to grow. Platform professionals interested in leading enterprise data are well positioned for these opportunities.
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