How To Create The Dream Data Science Team

datascience leadership Jan 21, 2021
All industries increasingly rely on data science as a standard part of the business today. Moreover, with the changing business environment, there is an increase as well in the quantity and variety of data available. This has lead to new and significant opportunities for businesses, where they make use of the knowledge from the data they have. That is why data science teams are now present in most companies, which helps them provide better internal and external business solutions.
 
A data science team can help to bring raw data through analytics and derive insights — critical in a competitive advantage. But, creating a data science team can be difficult, especially when roles vary and need high technical expertise. Creating a data team can also give rise to problems if the company (especially startups) don't exactly know where it will lead them.
 
If you or your company plans to create a data science team, you should consider thinking first about these questions:
  1. What do you need from a data team? Are there goals for them to work towards like solving certain business issues, or address customer concerns? What is expected from them to do in a set period of time?
  2. What will be the team role and composition? Who should you hire that helps reach the goal? Do you need a Data Engineer, Data Scientist, Data Analyst, or bring everyone in the mix? What is the eventual team composition that needs to be built?
  3. Do you have a team leader? Is there a strong team leader who can identify, attract, and recruit great data scientists? If there isn't someone available in the company, what qualities should you consider in hiring? Can he/she work with the top brass in defining the company’s data strategy?
  4. What are your hiring needs? When should you hire your first team? Is there enough work for them to do if you don't have a ton load of data yet? Do you need a generalist or a specialist?
  5. Do they fit your company's requirements? Are their career objectives align with the values of your organization? Do they fit what your company needs? Is the team required to work autonomously, or hand-in-hand with the management?
     

Hiring the right people

A data science team usually involves a lot of cross-functional skills such as prototyping or drawing helpful insights. Additionally, a solid hiring process will be vital in finding the right candidates. It can include a coding exercise, an in-person interview, or probationary involvement in a less critical data project. This helps assess their skills, professional background, and if they are a fit for the organization's shared culture.
 
If your company uses sample data projects as a primary evaluation for a candidate, you can consider the following:
  • We're their technical skills up to the standard (i.e. clear, flexible, etc.)?
  • Are the methods used appropriate for the project, and resulted as reliable?
  • Were they detail-oriented?
  • How were their communication and data-storytelling skills?
  • Did they ask appropriate questions in understanding the business case and application?
  • Were the results useful to the business?
Furthermore, as digital roles are changing along with increased adoption and advances in technology, job titles have become imprecise for different companies as some use different names for similar jobs. But based on the job/role function, the following are the essential roles to have for a data team:
  • Data Analyst serves as a gatekeeper for an organization's data so stakeholders can understand data and use it to make strategic business decisions. A Data Analyst collects and stores data on sales numbers, market research, logistics, linguistics, or other behaviors/categories.
  • Data Scientist that solves business problems using machine learning and data mining techniques. He/She handles statistical methods, processes, and algorithms to extract insights from data. His/her tasks may include processing, analyzing, experimenting, visualizing, and communicating the results.
  • Machine Learning Engineer has the role of designing and developing machine and deep learning systems, run ML tests and experiments, and implement appropriate ML algorithms.
  • Data Architect/Engineer handles finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise, as well as implement, test, and maintain infrastructural components for big data and large distributed systems.
  • Data Quality Officer inspects processes and equipment, maintain a data quality checklist, set data quality objectives, and also check the efficiency and functionality of these processes. He/She has a vital role in correcting erroneous data or address data gaps.
  • Data Science Manager is responsible for helping organizations leverage data, working with and through a team to provide valuable direction and insight, for management to make informed decisions. A Data Science Manager also helps showcase the team capabilities, coordinate team communications, keeps the team moving.

 

Growing and Managing A Data Science Team

While businesses are hiring more data professionals than ever, there are still some that struggle to realize the full organizational and financial benefits of having a data science team. This put pressure on some managers about carefully thinking about how talents are structured and managed.
 
Moreover, hiring capable people are only good at first if they can't achieve or deliver growth. That's why even if a small team is needed at first, the members should be able to deliver as much value to the organization as possible.
 
To tackle these key challenges, below are the steps to grow and manage a data science team:
  1. Build Trust
    Trust, authenticity, and loyalty are essential to good management. This doesn't exempt a team in data science, where confusion may arise around its role in the organization from unreasonable requests. Trust is important in letting your team know that you have their backs, and you know the value of their contributions. To build trust over time, be transparent about the good and bad things that may happen along the journey. It could be shown during recruiting, onboarding, day-to-day tasks, performance reviews, or when discussing the team’s, department’s, and organization’s strategy. It’s painful but critical for success. The moment you start “being nice” to avoid a tough conversation, you and your team have begun to lose.

  2. Have The Right Tools
    Tools play an important role that allows your team to automate and makes their job done faster. You should use relevant tools to do heavy lifting jobs, running scripts to automate queries, and save more time processing data that can make the team more productive. Also, automating repetitive weekly reports can help Data Engineers to focus on new challenging problems. Tools can also help check for data consistency, and share the work across the company with quick turn around time for further task processing.

  3. Establish A Processes
    Data science team projects are research-oriented, and it’s difficult to predict how long it will take for them to finish. Activities like model building and processing the data collection are usually done by a single person. Thus traditional collaborative work processes won't be efficient. Identify an approach that works best for your team, and can let them move on to a different task faster. Don't forget to test various options and see what best works for your team and projects.

  4. Focus on work
    Though doing activities that build relationships within a team is healthy, too much attention on it derails the focus of the team over what needs to be done. Especially when the team is flooded with requests for some analytics report, or other tasks. It’s important to focus on work and assign the right priority to these tasks to finish on time. By doing these, the team could then manage urgent requests better without sacrificing the time for important tasks. This doesn't mean too that they need to have a ton of work, rather have the chance to deliver quality output.

  5. Provide Feedback
    Feedback is positive and constructive criticism that is beneficial for letting the team know that they are doing something wrong, and see it as a piece of advice, not judgment. It can also make them feel you believe in them and want to help them reach the project’s goal sooner. Thus motivating them to do a good job, and creates a healthy communication flow. This also helps avoid major mistakes while fostering professional growth.

  6. Inspire To Be Data-Driven
    Always make sure that your team collects, uses, and processes accurate and quality data. Not only does it make your team's output reliable and trusted, but as well becomes a role model for others. This also puts your company closer to have a data-driven culture that acquires, processes, and leverages data in a timely fashion for efficient business solutions. Make sure that it is consistent, as it gives your team a positive feedback/impression too.

 

Having a team helps a company achieve its business goals, and you need to be picky in your hiring and be able to create a balanced team. Your team members will trust you, and they’ll understand how changes support the organization and its goals. Given how important data science has grown, it’s important to think about what a data science team can add to an organization, how they fit in, and how to hire them as well and build them effectively.

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