On the one hand, hiring a Data Scientist (DS) isn't that much different. Hey, a DS candidate is still a potential employee and all HR processes your company has in place should apply to this hire. On the other hand, Data Science is a unique, scarce recourse and a highly technological hire that not every company needs, can afford, or knows how to successfully onboard.
In this article, we provide specific advice on how, where, and when hire a DS specialist. Our advice relies on years of personal experience and AI domain expertise mixed with thorough desktop research mixed with. Here it goes:
First and foremost, it is worth investing some time in defining the problems and tasks that a Data Scientists would solve, their frequency, and urgency. This easy team exercise should help outline the scope of work and provide grounds for the specific job description. It may also save tens of thousands of dollars in unnecessary costs in case you arrive at the conclusion that you don't really need a full-time in-house DS person.
For effective results, a Data Scientist needs a certain ecosystem in place that includes a team of engineers, product managers and business leaders, and technical infrastructure to support his/her work.
Companies who fail in their AI efforts, and many of them do, end up blaming Data Scientists for poor skills whereas, in reality, it might be them who failed to provide the right support, environment, budget and team.
If you are new to the field, start with the research. Make sure you know the difference between ML Engineer, Computer Vision Engineer, Deep Learning Engineer, NLP specialist, and choose candidates with relevant experiences in your space. It is possible that hiring a professional with broad data science experience - team structure could be the anchor hire that could later help you compose the right team. Once you define the technical criteria, consider other factors such as personally to make sure they fit by culture in addition to the skillset.
Since Data Science is a relatively new field with a primarily young workforce, make the description fun and flexible.
You need to be flexible about job descriptions, experience, and offering remote work and telecommuting to expand the talent pool. Also, keep in mind that 45 percent of data scientists have five years (if not less) of experience, and that the field is mostly attracting young professionals.
The job description must include:
Our favorite instruments for Data Scientist outreach in Ukraine include Linkedin, djinni, Kaggle + Data Science courses (DRU). Keep in mind that the demand for this profession greatly exceeds the supply. So, act quickly, stay patient, and keep pushing.
The average time to fill data science positions (in the US) reached
62 days in April, with the hiring time for a senior data scientist taking
70.5 days on average.
For our recruiting team at DataRoot Labs, it takes on average 22.4 days for a DS role. We attribute our hiring speed to our specialization in AI, stellar market reputation as well as our own school community of Data Scientists (DataRoot University). Note those things for long-term recruiting and HR strategies.
While hiring is considered highly subjective, it doesn't have to be. Sort through candidates while keeping in mind your objective criteria such as experience, seniority, and cultural fit. A few extra tips on how to discern quality candidates are below:
Not sure how to evaluate candidates' skills accurately? Find an external consultant like DataRoot Labs with experience in doing just that.
The best candidates are likely to be headhunted halfway through the process in your company. Building a smooth, quick, and less exhausting flow with prompt feedback is an effective way to half your process as well as ensure an enjoyable candidate experience.
In 2020 companies compete for best candidates. Be ready that your candidate will be choosing among 5 different offers. While pay is not the decisive factor especially among mature professionals, it still matters. Make sure you know your numbers not to accidentally lowball your best candidates.
According to djinni, average Data Scientist from Kyiv with 1-2 years of experience and Upper-Intermediate level of English is earning
USD 500-2000 net per month. Companies are ready to pay
USD 3000-6000 per month to candidates with 5+ years of experience.
Anyway, it's still much less expensive to hire a DS specialist in Ukraine than in the US or Germany.
In the US the average data scientist salary there is
USD ~113k per year. In Germany, it's
EUR ~61.5k per year.
Source Glassdoor Job Search
If you need help with hiring a worthy DS candidates to join your project, feel free to reach to us at firstname.lastname@example.org.