When hiring a Data Scientist, is it necessary to include a LeetCode-style coding test?
My answer is NO. Here I am sharing my view in more detail.
Data science requires a distinct skill set that extends beyond the scope of typical software engineering problems found in LeetCode. These skills include statistical analysis, data visualization, machine learning, and domain knowledge, which are better evaluated through practical, real-world data challenges and problem-solving exercises.
Moreover, data scientists come from diverse academic and professional backgrounds, and a heavy focus on software engineering-centric coding tests may not accurately reflect their expertise in data-oriented tasks. It's more beneficial to tailor assessments to the specific data science role and its requirements, focusing on the candidate's ability to handle, analyze, and draw insights from data.
In the context of Bangladesh, as well as globally, the trend of relying on LeetCode-style coding tests is prevalent in the hiring processes for technical roles, including data scientist. However, companies need to consider alternative approaches that are more aligned with the specific requirements of data science roles. By tailoring assessments to evaluate a candidate's ability to manipulate, analyze, and derive actionable insights from data, organizations can make more informed hiring decisions. This approach not only benefits the companies in identifying the right talent but also provides a fairer, more relevant evaluation platform for job applicants whose strengths lie in data science rather than traditional software engineering.
Adopting this perspective in the recruitment process is a step towards recognizing the unique skill set required in data science and ensuring that the hiring practices are aligned with the demands of the role. It's a shift that could bring mutual benefits to both employers and job candidates, enabling a more effective and efficient match between data science talents and the roles that require them.
Data Analyst| Google Sheets| SQL| Python| Power BI| Microsoft Fabric
1 年no
Software Architect | Product Coach | Data Scientist | Computer Visions | SSBB
1 年For a DS its more important to understand the data behaivior rather than knowing how to play around with it with complex algorithms. A good level of coading knowledge specifically on data structure would be more useful for data pre process and transformation tasks. So be more specific what kind of skillset you want to bring in with the new recruit and set the problems accordingly for your interview.
AI, QC Researcher, Research Supervision, Power sector monitoring
1 年DL ? coding ??? competitive ????? statistics competitive ???? ?????
Ex-Cisco @ Silicon Valley | UCL Graduate | Data Scientist @ SSL Wireless
1 年No. The optimal method for evaluating candidates involves initially conducting an assessment to gauge their fundamental knowledge and experiences. This should be followed by a take-home assignment, which the candidates will later present. Finally, a behavioral round could be conducted as the concluding part of the evaluation process.
Senior Data Scientist @ Cognite | ML Researcher | ML Consultant
1 年Practically, it depends on the position itself. In general, there is not much necessity of having a competitive programming test. But if the position requires a lot of code and algorithm implementation by hand rather than just utilizing library functions, surely the position requires a lot of knowledge of optimized algorithm development strategy. And trust me, the algorithm implementation is not so uncommon for a data scientist these days.