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How to Lead Data Science Teams to Collaborate Effectively

To motivate others to achieve the team’s mission and vision, you should possess the potential to motivate the inner prodigy inside you. Yes, you should be willing to learn and guide others to be a learner for life. That is the spirit of the most looked upon manager or team lead across any team.

You should be an efficient individual contributor with a flair for constant updating and being mindful towards overall excellence. Remember, the best data scientists would be intolerant to bad managers. A bad manager or a team lead can hold on to negative traits like micromanagement, gaslighting and involving in office politics. These are the characteristics dreaded by any data scientist, be it a junior level data scientist or a much senior employee.

All they look for is a resilient and experienced manager to direct them on the right path, or as the businessman Lee Iacocca once said: “Management is nothing more than motivating other people”.

As the manager of a data science team, you might agree with Lee Lacocca and ask yourself the following questions:

  • What is the secret sauce to better team integrity that the new-age managers must know about?
  • How to get your team members to collaborate efficiently?
  • What can let them upscale as a manager, and let others under their team thrive?

In this blog post, we have identified four factors that answer these questions and which you can take into consideration to promote a high team performance.

 

1. Foster collaboration through trust and integrity

Data scientists spend around 80% of their time on capturing the meaning out of the data: decoding the datasets, conducting ETL as well as handling initial feature selection. The rest of the time is spent on making the data and their work available to the team.

That’s why there should be a strong sense of trust among your teammates. They should not feel that heat when they approach each other. As a team manager or a lead, you would be responsible to demystify all the confusions related to the field. Be it handling unreasonable requests or explaining every team’s role in the organisation, encourage people to reach out to the right authority without any hesitation.

Be it recruiting, onboarding, or handling performance reviews, you should be leading the team by building a sense of candour. Being “candid” need not translate directly to being “nice”. You should know when to be straightforward and point out any blunders before any major mishaps leap up in the team. Encourage this attitude within your team, instead of allowing them to sugar coat stuff.

 

2. Connect with your team smartly to work wonders

To become the best team working on data science projects, allow your team to clearly understand the business goals. You should be able to lead your team’s efforts to greater heights with regards to your organizational strategy. This is the most vital task imbibed upon any manager in the data science domain.

Data science projects are usually an amalgamation of questions from people outside the team. The questions put forth by the team would demand deeper fine-tuning and discussing with stakeholders. You should understand all the information you need to form a clear objective to proceed.

In her podcast interview with Datacamp, Angela Bassa talks about the importance of providing space and time for the company’s data science function to measure. This would allow you to organize the processes to deliver better output. Your team excels much better when transparency and openness are prevalent. Every team member should be open for a candid conversation whenever a team member approaches them. There should not be any professional inhibitions between, let us say, a product manager and a data scientist within the team.

It’s very probable that your stakeholders aren’t able to answer all the questions by themselves and that they don’t have a proper idea of how your final data science product exactly looks like. To ensure a general awareness of the project goals, invite your data science team to all your product and strategy meetings. In this remote working age, you can arrange a team chat to allow creative processes to flow in the team.

 

3. Create proactive teams

Being the hot cake in the market, data science is no wonder the most wanted career choice for every graduate. Your HR manager can get plenty of applications from candidates. You must cherry-pick the right candidates to make sure they survive in the long run. It is always good to create and streamline the right hiring process to sustain in the long term.

Be that forward-looking manager who values diversity among candidates. From their technical skillset to domain expertise, ensure that they are ready to fit in the professional experience and academic caricature expected to become the most prominent data scientist. They should also possess working experience on tools like SAS, Apache Spark, MATLAB and Tableau.

Do not forget to look for candidates who have wide practical knowledge in physics, computer science and statistics. The candidates should be people who can play around with stories related to data. They should be visualization wizards with a knack to collaborate and excel.

Also, concepts like Team Data Science Process (TDSP), the agile development modification of CRISP-DM, involving a modern data science lifecycle process to suit the cloud-oriented processing can come handy to foster proactive collaboration.

TDSP, introduced by Microsoft, is an iterative, agile data science methodology through which we can deliver predictive analytics solutions along with intelligent applications with efficient models. 

The silver bullet of TDSP is that it helps you one-up your team collaboration spirit. You can learn through suggestions on how to build a proactive team.

 

4. Update yourself and your team

Trends keep changing and shifting every day. It could be Business Intelligence, Artificial Intelligence and Machine Learning. Have a tip-top idea on predictive modelling and every related field. Staying updated is the only way to guide your team for escalated growth.

Professor Roger M. Stein from MIT points out that managing data scientists is quite different compared to managing any other team as it requires particular philosophies and skills. He also states a situation where organizations would ignore all the costs that low-cost (and low-quality) analytics projects take up. While data science within any business requires financial discipline, only most experienced data scientists are aware of the importance of updating themselves.

When you lower on quality aspects, it can have a long-term implication on the aesthetic and abstract values of the project. Decision making becomes sloppier in that case. Not every project would require high-cost and robust efforts. A few projects can also get over when you leverage a few ready-made solutions.

To segregate the type of projects concerning these differences, your team should have a clear knowledge of the nuances of a data science project. Add to this an effective communication strategy, accepting constructive criticism and validated learning. Your team now has the recipe to nail any data science project like a pro.

Keep updating yourself regularly. Read more blogs, be active in communities like Kaggle and Stack Overflow. Ensure that your knowledge and skillset are up to date in the current market. Encourage other team members to keep updating themselves this way.

Let knowledge sharing be a normal occurrence in your team.

 

To sum it up

Managing data science teams successfully is challenging and interesting at the same time. It’s an everyday task, which continuously contributes to your learning curve. Fostering collaboration within your team, communicating project goals transparently, creating proactive teams as well as keeping oneself up to date are essential factors that contribute to outstanding team collaboration and a strong team spirit.

Written by

Manasi Vartak

Founder and CEO at Verta; PhD in computer science from MIT CSAIL; Creator of ModelDB, open-source system for machine learning model management.

Verta

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