Gru's Talent Strategy: Checklist for Building Your Dream Team
Tags: datascience, talent, culture
Published: 2024-01-29 18:24:16

**Intro**:
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](https://impact.economist.com/new-globalisation/seizing-the-technology-imperative/case-study-7-eleven-japan) 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](https://hdsr.mitpress.mit.edu/pub/wfr9k3vq/release/1)
[HBR](https://hbr.org/2013/12/you-may-not-need-big-data-after-all)