How To Delegate Tasks In A Data Science Team
Having a large data science team with experienced professionals doesn’t guarantee that a company will get the best results. To witness the best results from your data science team, you first have to figure out which professional should do what work. The delegation of tasks is an art that every team leader needs to master. In this article, we are going to take a look at some of the tips and methods organisations can use to make the best out of their A-team. Data Science Unit Structure One of the best ways to learn how to delegate work in a data science team or unit is by understanding and forming a structure for the team. If we talk in general, a data science unit in an organisation might have more than one data science team and with different data science roles. Microsoft has come up with a  framework called The Team Data Science Process (TDSP) which is a structured methodology for making the best out of a data science team. And according to the tech giant, there are four distinct roles: Group Manager: S/he is the one who manages the entire data science unit in an organisation. Team Lead: A data science team lead is responsible for managing a team of data science professionals working on a project. Project Lead: S/he is responsible for keeping track and managing the day to day work of the data science professionals on the project. Contributors: These are fellow data scientist who does the actual job on the project. While each of these professionals plays a vital role in the entire project, it is important to understand that when it is about a data science team, a professional might have to work on more than one task, or a task might have more than one professional. However, to make the teamwork on seamlessly, organisations must consider going through some steps. Things To Consider Delegating tasks among contributors or fellow data science professionals is a tricky job. The one dividing the tasks have to be aware of the strengths and skills of each individual. Talking about contributors, there are some main roles — data scientists, data engineers, the visualisation expert, business analysts, machine learning engineers etc. And the tasks have to be divided keeping in mind what each professional does. “If you have too many PhDs running around on a problem and poor management, you’ll get the “too many cooks in the kitchen” effect,” a Redditor posted on a thread. Sort Out Work As mentioned, there are different professionals with different skill sets and roles. It is imperative to sort the professionals in order to allot tasks for better results. For example, a data scientist is usually responsible for building, tuning and testing algorithms and models. And s/he cannot be allotted with tasks such as building a data-driven platform, which is basically the role of a data engineer. Assess Each Professional Based On His/Her Role This might seem to be a little time consuming, but it is undoubtedly one of the best ways to make the most out of your data science team. Follow different methods and strategies of assessment and figure out which data science professionals are best for prime jobs. Find The Perfect Project Lead Finding the perfect fit for a project management role is not an easy task. While Data science contributors work on projects, the project manager needs to report, plan and ensure progress. Also, s/he has to keep updating the stakeholders (even about small insights ) and make sure that they don’t get frustrated by a perceived lack of progress. And this is where the assessment comes into the picture. Just like for every other role, there has to be an assessment for the management role. Always Consider Giving An Opportunity To Others This case doesn’t happen all the time. However, it is considered to be a best practice to give an opportunity to other data science professionals as well. For example, if you have allotted a professional for the prime task based on their assessment, but there is one more professional who has done well. So make sure you give a chance to that professional as well. You never know, the second option might turn out to be the best one. Periodic Pauses Periodic pauses are important for companies to take a look at how their data science teams are performing, whether every project is being done on time, without any faults etc. Big companies often conduct periodic pause and assess each professional to argue about who does what. This helps in keeping the work going with fewer faults because, during every pause, necessary steps are taken to rectify mistakes (if there are any). Team Inside The Team While companies mostly focus on delegating work to each individual, there are companies that take a whole new approach by dividing the team into two teams — the ones who make sense of data and the ones who use data in production. This strategy helps in task allocation. Therefore, tasks such as data cleaning, forecasting, modelling, visualisation, etc. can be allotted to one team and tasks like building recommendation systems, personalisation use cases, etc. can be given to the other.
Having a large data science team with experienced professionals doesn’t guarantee that a company will get the best results. To witness the best results from your data science team, you first have to figure out which professional should do what work. The delegation of tasks is an art that every team leader needs to master. […]
To witness the best results from your data science team, you first have to figure out which professional should do what work.
["Data Science", "Data science team"]
["data science", "Go", "machine learning", "Data science team", "AI", "ML", "recommendation systems", "ViT", "Rust", "GAN", "Data Science", "R"]
["AI", "machine learning", "ML", "data science", "recommendation systems", "R", "Go", "Rust", "GAN", "ViT"]
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