Introduction
The role of data scientist is the fourth-fastest growing job in the US, according to 2023 data from the US Bureau of Labor Statistics, with data science employment expected to grow by 36% from 2021 to 2031. Why are so many organizations looking to hire data scientists? Data provides valuable insight into all business areas, from customer behavior to market trends. And these answers can only be uncovered with the right expertise.
Before your business can harness the power of data science expertise, you must consider the importance of each step of the hiring process, from screening to onboarding. This edition of The PTP Report serves as your one-stop guide. Keep reading, and you will be on your way to acquiring the best data science candidates and keeping them onboard to help your business make smarter decisions more efficiently.
Define Your Unique Data Needs
Talented data scientists can unlock a wealth of opportunity for your business, arming decision-making with tangible insights.
Some practical ways data scientists can transform your business include:
- Analyzing business transactions and procedures by finding patterns in large amounts of data with tools like SQL.
- Supporting decision-making predictive and prescriptive analytics, empowered by AI technology.
- Making accurate business forecasts using different types of data, including sales statistics, customer service data, and market trends (taking note of anomalous data).
- Reducing customer turnover by analyzing customers’ interaction preferences and consumption habits.
[Is your business intelligence making you dumb? Learn best data practices for BI in this PTP Report.]
Determine Your Data Science Budget
In the US, the median salary for data scientists is approximately $108,020, according to the US Bureau of Labor Statistics. Depending on factors like industry, skills and qualifications, experience, location, and job responsibilities, the cost of hiring a data scientist can range from $60,000 to over $200,000 a year.
Write a Clear Data Scientist Job Description
When it comes to technical roles with niche skill requirements, it’s best to keep job descriptions straightforward, avoiding unnecessary jargon. Simply state the purpose of the role within the context of your organization, specifying required hard and soft skills, as well as proficiency in relevant tools like Microsoft Excel, TensorFlow, and Python. Additionally, clarify expectations ranging from day-to-day functions to cultural dynamics to employee benefits.
Choose the Right IT Talent Source
There are multiple routes you can go for sourcing data science talent. One option is to post on a job board, preferably a tech-specific one like Dice or Stack Overflow. Another option is to partner with a data science recruiting service. e efficient and effective. IT staffing firms like PTP keep up with relevant industry trends and have an existing pipeline of talent, making hiring and onboarding mor
Ask the Right Interview Questions for Data Scientists
From data science fundamentals to business acumen, interviews should cover a wide range of relevant technical and behavioral skills. Some sample questions include:
- Why is data cleaning crucial, and what are your go-to methods for cleaning data?
- What are some ways the rise of AI is impacting your work as a data scientist?
- Have you ever had to explain the technical details of a project to a nontechnical person? How did you do it?
[Are you a professional looking to pivot into data science? Learn more in this PTP Report.]
Assess Relevant Hard and Soft Skills for Data Scientists
As data science transforms with the help of emerging technology, so do its skill requirements. And while technical skills are key, soft (or behavioral) skills shouldn’t be glossed over, especially as rapidly evolving technology requires adaptability and continuous learning. All skills can be measured using standardized assessments, from take-home assessments and live coding assignments to situational interviews and video-recorded explanations.
Onboard Data Science Talent with Care
We’ve already determined that hiring data science talent is a worthy investment. With thoughtful onboarding, employers can ensure talent feels the same way. In addition to a competitive compensation package, candidates will also want development opportunities and access to cutting-edge tools and technologies. When professionals feel valued, challenged, and well-equipped, they can stay committed to doing their best work, allowing both the employer and professional to get the most out of their investment.
Conclusion
Hiring and retaining top data science talent requires a thoughtful approach at every stage, from clearly defining your needs to creating a seamless onboarding process. By prioritizing a balance of technical and soft skills, leveraging the right talent sourcing methods, and offering competitive packages, your organization can unlock the full potential of data science.
