In a recent LinkedIn poll we explored the extent to which companies were struggling to locate data talent in what is a fierce battle for data talent. So, what is behind this?
Over the last decade or so, data science has seen a dramatic ramp as a rapidly growing and highly sought-after field in business. Well, we would say that wouldn’t we? But its relative newness and diverse applications across industries have created real challenges for organisations seeking to hire the right talent. The, as yet, lack of well-defined career paths and standardised job roles and criteria has led to an incredibly dynamic and complex landscape of data scientist type roles, each with their own unique set of skills and experiences.
The Unicorn Hunt: Companies' Struggle to Find the Right Data Talent
Companies frequently struggle to find a data scientist who possesses a unique skill set spanning business, software engineering and statistics, appearing almost like a mythical figure. Nevertheless, not all businesses are searching for identical skill sets when seeking a data scientist. Analytical skills may be preferred by some to facilitate business decision-making, while others may emphasise software engineering and impactful statistical modelling to empower their business growth.
The difference in job descriptions and candidate expectations can result in dire hiring mismatches, thus challenging companies to find the right fit for the position. Organisations need to clearly define their specific requirements and effectively convey the job expectations to potential candidates but this is proving to be a real challenge.
Overcoming the Challenges: Strategies for Attracting Data Talent
To navigate the competitive landscape of data science talent, organisations must work closely with their human resources teams to define the precise skills and traits they are seeking. Being open to non-traditional candidates with the potential to grow into the role can expand the pool of potential hires. In our experience, diversifying recruitment sources beyond the usual internet job sites, such as attending local meet-ups, user groups and developing relationships with academic programs, can also help surface hidden talent. This is the premise on which we’ve build are entire network!
Quantifying the cost of an open position relative to the cost of additional talent search and recruitment efforts can help organisations make informed decisions about loosening position requirements or investing in training for candidates with genuine potential. Conveying the exciting aspects of available projects and the opportunity to make a significant impact in the form of quality narrative storytelling can also entice candidates to quickly develop the necessary skills.
The Future of the Data Science Talent Market
Nobody will argue that the demand for data science talent is expected to continue growing. This is driven by the increasing volume, availability and detail of information captured by enterprises, the rise of multimedia, social media and the Internet of Things. However, the supply of skilled practitioners has not kept pace with the commercial sector's requirements, despite the proliferation of university courses in data science.
The participation of tech giants in recruiting from graduate programs has added tremendous strain to the talent market, with companies like Amazon paying PhD graduates in AI and machine learning significant sums to remain in university research roles. The acquisition of small analytics firms following an acqui-hiring strategy to internalise skilled personnel has further compounded this talent shortage.
The interdisciplinary nature of data science and its methodological underpinnings also contribute to the difficulty in finding candidates with the right combination of skills. Many argue that a lot of the data science masters programs are merely repackaged statistics degrees that produce graduates ill-equipped to program and lacking exposure to big data technology (you should note, this just isn’t the case in Scotland but beyond that… well you need to do some due diligence)! On the other hand, computer science graduates may lack a grounding in statistical theory and be more accustomed to working with synthetic data in software engineering roles. PhD graduates in quantitative fields may have the desired research skills and specialisation in certain methodologies but could be overqualified and underexperienced in industry practice.
Conclusion
The challenges of finding data science talent are complex and multifaceted, stemming from the field's relative newness, diverse applications and the unique combination of skills required and in demand.
Organisations must be strategic in their approach to hiring, clearly defining their needs, diversifying recruitment sources and being open to investing in training for candidates with potential. As the demand for data science talent continues to grow, companies that can effectively navigate the competitive landscape and attract the right individuals will be well-positioned to harness the power of data and drive business value.
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