How To Manage a Data Science Team in 6 Steps

How to manage a data science team
  1. Choose a team structure. …
  2. Assign specific roles to team members. …
  3. Engage with stakeholders. …
  4. Create a positive team culture and work environment. …
  5. Help team members develop their skills. …
  6. Develop your own professional leadership skills.

You’ve probably seen the unwavering recommendation to start using machine learning if you’ve been following the advice of experts in data science and predictive analytics. The best course of action, according to James Hodson in the Harvard Business Review, is to go after the “low hanging fruit” before scaling up to gain expertise in more complex operations.

Just recently we talked about machine-learning-as-a-service (MLaaS) platforms. The main takeaway from the current trends is simple. As machine learning gradually becomes a commodity, it becomes more accessible to midsize and small businesses. The top vendors, including Google, Amazon, Microsoft, and IBM, offer platforms and APIs to run fundamental machine learning operations without a private infrastructure and extensive data science knowledge. Using this lean and inexpensive strategy in the beginning would be the best course of action. A team structure can be changed as analytics capabilities grow to speed up operations and expand an analytics toolkit.

The most prosperous data-driven businesses tackle complex data science tasks, which may involve research, the use of numerous ML models tailored to different decision-making criteria, or a variety of ML-supported services. Large organizations can use data science teams to support various business units and work in their specialized analytical areas of interest. Data science teams are obviously all very different because they are each uniquely constructed and wired for particular tasks.

Let’s look, for example, at the Airbnb data science team. For a deeper understanding of how Airbnb creates its culture, you can watch this talk by its data scientist Martin Daniel or read a blog post from its former DS lead, but in brief, here are three key principles they follow.

Let’s talk about data scientist skill sets. Sadly, the term “data scientist” has grown and become too ambiguous in recent years. There is no agreement on the skill set of a data scientist that has emerged since data science entered the business spotlight. “When I hear the term data scientist, I tend to think of the unicorn, and all that it entails, and then remember that they don’t exist, and that actual data scientists play many diverse roles in organizations, with varying levels of business, technical, interpersonal, communication, and domain skills,” says Matthew Mayo, a data scientist and the deputy editor of KDNuggets. ”.

Chief Analytics Officer/Chief Data Officer. This crucial leadership position was broadly covered in our whitepaper on machine learning. A “business translator,” or CAO, fills the gap between domain knowledge and data science by serving as both a technical lead and a visionary. Looking at the below visualization might help you understand this better.

Data analyst. The data analyst’s job requires proper data gathering and analysis techniques. An analyst interprets the analytics findings and makes sure the data is comprehensive and relevant. Some businesses, like IBM or HP, also demand that data analysts have visualization abilities in order to transform threatening numbers into concrete insights via graphics.

Data scientist (not a data science unicorn). Assuming you aren’t searching for unicorns, a data scientist is a person who uses machine learning and data mining techniques to solve business problems. In the event that this is too ambiguous, the role can be reduced to data preparation and cleaning with additional model training and evaluation.

By placing data output in the appropriate context, data journalists assist in making sense of data. They must also articulate business issues and create compelling narratives from the analytics data. Despite being required to have experience with coding and statistics, they should be able to communicate the idea to stakeholders and represent the data team to those who are not familiar with statistics.

Data architect. Working with large amounts of data—you guessed it, big data—requires this position. This position is crucial to store the data, define database architecture, centralize data, and guarantee integrity across various sources, though, if you don’t solely rely on MLaaS cloud platforms. Performance is another responsibility of the architect for large distributed systems and large datasets.

Application/data visualization engineer. In essence, this position is only required for a specific data science model. In other instances, IT units send software engineers to deliver data science outcomes in user-facing applications. Additionally, it’s very likely that an application engineer or other front-end unit developers will be in charge of user data visualization.

Along with the general lack of experts, the first difficulty in finding talent in data science is the high salary expectations. According to the O’Reilly Data Science Salary Survey 2017, the median annual base salary was $90,000, and at the time this article was updated, it was $112,774 in the US. Geographical location, particular technical skills, organization size, gender, industry, and education all have a significant impact on these numbers. Additional difficulties include engagement and retention if you choose to hire qualified analytics experts.

Without a doubt, the majority of data scientists aspire to work for a company that has intriguing problems to solve. But not every company is Facebook, Netflix, or Amazon. However, organizations hire data scientists for entry-level positions due to the need to complete data-related tasks. Therefore, as Daniel Tunkelang suggests, hiring a generalist with a strong STEM background and some experience working with data is a promising option on the early stages of machine learning adoption. This strategy suggests switching to powerful, narrowly focused specialists later on. They would regularly advance their systems and swap out outdated algorithms for new ones.

The creation of approachable machine learning platforms that would welcome new IT personnel and permit further scaling is another method for addressing the talent shortage and financial constraints. Some businesses get around this obstacle even if they are unable to hire experienced data scientists by establishing connections with educational institutions. In the US, there are about a dozen Ph. D. Numerous data science-focused programs and boot camps with roughly 12-month courses are also available.

Leading Data Science Teams: A Framework To Help Guide Data Science Project Managers – Jeffrey Saltz

Why is having a data science team important?

Having a data science team is crucial because data analysis calls for specialized knowledge and abilities, and effective data analysis can assist businesses in lowering costs, increasing revenue, and identifying their target market. Additionally, data scientists can assist a company in streamlining operations and spotting trends that have an impact on productivity. Taking charge of a group of data scientists can make your workforce more effective and allow you to monitor their objectives and progress.

What is a data science team?

A group of data scientists who collaborate on data analysis and interpretation is known as a data science team. Businesses use data to comprehend trends and how they may affect their operations. Together, data scientists gather and examine that data to produce a concrete outcome, such as an explanation of trends or graphs. They typically have excellent analytical, mathematical, and critical thinking abilities. They become an invaluable asset to a business where team members can cooperate to utilize their combined skill sets.

Data science teams typically perform the following duties:

How to manage a data science team

Managing a data science team often requires practice and skills. You can effectively manage the team by following these six steps:

1. Choose a team structure

Your data science team’s chosen team structure can help you increase productivity and build an accountability network with business executives and stakeholders. Additionally, a team structure aids in defining roles for team members and distributes tasks according to positions and skills. Consider the following examples of typical data science team structures:

2. Assign specific roles to team members

You can assign roles to each member of the data analytics team after selecting a team structure based on the needs of the company. Your employees’ key competencies are highlighted in their individual roles, where they can be most useful. It also creates accountability for each team member. The following are some typical tasks you can give data science teams:

3. Engage with stakeholders

Engage with the stakeholders to improve communication between the data team and the stakeholders. This promotes trust and ensures that the objectives of the business’ operations are shared by both sides. A company’s stakeholders frequently request project updates, necessitating regular communication between stakeholders, data science teams, and other business operations specialists. When a project starts, inform the stakeholders and give them a chance to ask questions during meetings, email threads, or phone calls. Greater communication and teamwork within the organization’s departments can foster a culture that is more team-oriented, which could increase revenue and productivity.

4. Create a positive team culture and work environment

A supportive and professional work environment and team culture can help you maximize your team’s abilities. By emphasizing values like honesty, integrity, punctuality, professionalism, and innovation, managers can foster a positive team culture. To set a positive example for your team during daily tasks and projects, concentrate on demonstrating these values. To demonstrate your support and willingness to make adjustments to ensure everyone is at ease and content, pay attention to the needs and concerns of your team.

5. Help team members develop their skills

Having a strong team can help the group be more creative and produce better work. As a manager, you can assist your team in strengthening their abilities by providing coaching or professional mentoring from a leadership perspective. This entails emphasizing strengths development and addressing team weaknesses. For instance, if your team excels at meeting deadlines but could use some practice managing their stress, you could concentrate on helping them. A more trusting relationship between the management of the company and the employees can result from encouraging team members to develop their skills, which can also lead to increased productivity and innovation.

6. Develop your own professional leadership skills

One of the best ways for a team leader to oversee a data science team is to concentrate on developing your leadership abilities professionally. Employing mentors or business professionals as professional coaches can help you develop your leadership abilities. You can also enroll in classes or seminars on leadership that emphasize these concepts. Develop your leadership abilities, particularly those related to coaching, communication, and listening. These abilities can enable you to establish relationships with your team and sharpen your leadership style. You can also advance your technical data science and analytics skills to enhance business data analytics and spur innovation.


How can data science teams be successful?

Establish a culture of learning and innovation that pushes team members to think creatively about problems and issues in the workplace. Encourage the data science team to work closely with the business units they support by promoting analytics projects that do so.

What is the most effective way to structure a data science team?

Recommendation: Centralized reporting is probably the simplest place to begin for a startup looking to build a strong data culture. Avoid creating knowledge silos by using embedding to make sure data scientists are working on projects that are beneficial to the company.

What are the three keys to data science team success?

You must ensure that the team is composed of competent individuals who can perform the three crucial roles of data engineer, machine learning expert, and business analyst if you want your data science project to succeed.

What roles do you need in your data science team?

They should be proficient in data literacy, with the capacity to comprehend, analyze, and communicate in data. Finally, for the team to work effectively and with their clients, they should all have excellent communication and presentation skills. Artificial intelligence and machine learning are currently very popular.

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