Data Science - A Hiring Strategy
How to effectively and continously hire data scientists
By Steven Thomas
Evaluating a candidate to become a member of a data science team is challenging and the sample size of contenders for vacancies drops fast, experimentation seems impractical, and the biases in interviewing are orders of magnitude that are more stark than when hiring general analysts.
With the shortage of data scientists, many are moving across from academia in fields of advanced education such as mathematics and economics, attracted by the high salary levels and interesting problems that big business is looking to solve.
Contemporary challenges and opportunities presented to companies today are wildly diverse and range from gleaning consumer insights from POS (point of sale) data, to manipulating social media interactions and user-submitted photos into revenue generating binary code.
According to our data at Avellio, authentic data scientists with hard skills in statistics, coding, along with domain experience are amongst the most coveted professionals in business, with Ph.D qualified data science leaders holding maths, statistics and engineering qualifications securing salaries of upwards of €300.000 or more from private companies and financial institutions. While the average data science role pays around €82,500 at a median point, rising to €130.000 in the 90th percentile for those advanced analytics professionals with 8 years+ experience.
We have also seen a rise in client requirements from companies looking to attract and secure ‘business critical’ data science experts, with a doubling in job orders and search assignments from companies looking to hire data science experts to add to their analytics and insights capabilities.
Sub-sets of data science are starting to break away from core analytics functions within organisations and companies, with social media being a huge area of interest for companies, where specialist analytics roles that focus on ‘analysis of sentiment’ are starting to evolve, this is better described as identifying ways to establish the numbers of tweets posted that are either commending your company or slating it.
Our clients are aware that top drawer talent in specialist fields such as machine learning are receiving offers of employment on a daily basis, it is a hugely competitive landscape, and the ‘war for analytics talent’ is probably the single biggest threat to business in order to remain competitive, ensure client and customer retention, and to enable future growth.
One of the ways that we can help our clients to attract the right calibre of talent is by working with them on the creation of a job specification that appeals to the professionals that they are seeking to attract, aside from the allure of the role or project that the candidate will become engaged upon, additional perks could be offered, such as flexible home working, performance related bonuses, and attractive work spaces and locations, it is crucial that hiring managers are creative in their thinking, in order to highlight their points of difference.
Companies are increasingly looking to attract analytics talent at the source, by partnering with universities to augment them with class projects, enabling them to strike relationships up with the next wave of maths, statistics and engineering graduates that have a bent for working with big data. Companies are important bedfellows for universities as they can provide vast data lakes of anonymised real world data, that students can set to work on for insight discoveries, such as consumer credit risk analysis, for example. It is a smart motive of those companies as a means towards establishing relations with the future data science leaders of tomorrow, by nurturing them end-to-end.
Another vitally important aspect of ensuring the on-boarding of new advanced analytics talent, especially so with experienced hires, is a contraction of the hiring process. Many big companies now have the sign-off to hire analytics experts in quick time in order to take them out of the market and away from competitors. It is not enough to be in a position to offer a great job opportunity alone, as speed is of the essence, with first interviews leading to subsequent next-day offers of employment not uncommon.
The future success of companies today requires the agility and drive to constantly rethink, react and reinvent the hiring proposition.
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'Data is emerging as the key differentiator in the machine learning race, because good data is uncommon'. #BigData