Bala MadhusoodhananIntro:
Almost all the organisation are investing with the belief that data and analytics are key for transforming their business. Not all data transformation stories are success as most organisation struggle to do a good job with the information that they already have. They get into the hype cycle and purse on finding problems to make use of the technology. Just because of heavy technology investment does not mean the success is for granted. The key is having the right skill set / team / people who can learn how to use the data already available to drive efficiency and improve the operational decision. Great example would be the 7-eleven story where the success for the business growth was on the ability of good people to use good data (little data) to make impactful decisions.
Lets explore some of the key attribute that I personally was looking for while building my team.
| Theme | Check list |
|---|---|
| Education Background | 1) Resume is a good start for this. The value of qualifications from reputable universities or specialized programs 2) If the candidate has put additional effort as part of his learning goals by doing certification on MOOC platform. It’s a positive as the attitute to unlearn / learn is very key |
| Data science Experience (Research experience / On the job training) | 1) Resume would highlight any masters / research project that the candidate have upskilled with. Have conversation about that and understand the impact the research would have made. The conversation woudl help to understand their ability to translate theoretical concepts into practical applications. 2) Pick a project called in the resume and tease out what the candidate approach to solve that problem was. Look out for if the candidate is calling of the impact of that project ( Savings or ROI etc..) |
| Breath of understanding ((e.g. forecasting, predicting, optimizing, simulating, etc) | 1) Look for the projects listed on a candidate's resume, focusing on those where they have applied specific techniques to address real-world challenges. 2) Give domain specific problem similar to one or 2 of the project and understand the critical thinking and problem solving ability. By simulating scenarios resembling their past projects would help me to understand the candidate's ability to apply their expertise to diverse challenges, showcasing their problem-solving agility and the practical impact of their analytical skills.The simulation also would help to understand the domain knowledge |
| Leadership experience (Coaching, mentoring, and line management experience) | 1) Resume generally would indicate the title ( responsibilities, such as team lead, manager, or supervisor). Understand if the call-out with respect to scope of their responsibilities to understand the level of management involved. Look for any specific achievements related to team leadership, coaching, or mentoring called in the resume 2) During the conversation with the candidate start with a general discussion about their career path and progression to understand their leadership journey.Pose hypothetical scenarios to gauge how they handle team-related challenges. For instance, "How would you address conflicts within your team?". Inquire about instances where they've mentored or coached team members "Can you share a specific example of a team member you mentored and the impact it had?". Explore their ability to delegate tasks and empower team members. "How do you encourage autonomy and responsibility within your team?" |
| Programming Exposure (R / Python / C++ / Scala) | 1) Depending on the role you could may be give a small coding challenge and ask them to share their analysis / code . This would give an insight on how much aligned are they with standards and practice. This would also enable you to have conversation to understand their rationale on explaining the approach 2) Searching for their public repo would be another option |
| Impact of the Data Science experience ( Projects and the value that the project provided) | Ask for candidate to provide an overview of a few key projects they've worked on, emphasizing their role and contributions.Request specific metrics or outcomes achieved. "How did the project impact key performance indicators or business metrics? Can you provide quantifiable results?". Explore the broader value proposition of the projects. "Beyond immediate outcomes, how did these projects contribute to the overall strategic goals of the organization?" |
| Tools and Process knowledge (Agile / JIRA etc..) | Look out of resume callout on tools and process like JIRA / Agile to ensure awareness of these tools can enhance productivity and collaboration. |
| Continous Learning mindset ( evidence of MCOO, Conference, blogging etc..) | Ask about evidence that showcase a candidate's commitment to learning (MCOO, conferences, blogging, etc.). |
Building a great team requires the ability to understand what motivates people and then using that knowledge to encourage collaboration. I would love to hear your thoughts on how to build a dream team for data science.
Further Read
Harvard Data Science Review
HBR
