Working Together on a Data Science Team: Successful Data Science Teamwork
As I explained in a previous newsletter a data science team should consist of three to five members, including the following:
Together, the members of the data science team engage in a cyclical step-by-step process that generally goes like this:
Data science teams also commonly run experiments on data to enhance their learning. This will help the team collaborate on many data-driven projects.
Experiments generally comply with the scientific method:
Example
Suppose your data science team works for an online magazine. At the end of each story posted on the site is a link that allows readers to share the article. The data analyst on the team ranks the stories from most shared to least shared and presents the following report to the team for discussion.
The research lead asks, “What makes the top-ranked articles so popular? Are articles on certain topics more likely to be shared? Do certain key phrases trigger sharing? Are longer or shorter articles more likely to be shared?”
Your team works together to create a model that reveals correlations between the number of shares and a number of variables, including the following:
The research lead is critical here because she knows most about the business. She may know that certain writers are more popular than others or that the magazine receives more positive feedback when it publishes on certain topics. She may also be best at coming up with key words and phrases to include in the correlation analysis; for example, certain key words and phrases, such as “sneak peek,” “insider,” or “whisper” may suggest an article about rumors in the industry that readers tend to find compelling. This will create a visualization that can communicate even big data to people without a data skill set.
Based on the results, the analyst develops a predictive analytics model to be used to forecast the number of shares for any new articles. He tests the model on a subset of previous articles, tweaks it, tests it again, and continues this process until the model produces accurate “forecasts” on past articles.
At this point, the project manager steps in to communicate the team’s findings and make the model available to the organization’s editors, so it can be used to evaluate future article submissions. She may even recommend the model to the marketing department to use as a tool for determining how to charge for advertising placements — perhaps the magazine can charge more for ads that are positioned alongside articles that are more likely to be shared by readers.
Striving for Innovation
Although you generally want to keep your data science team small, you also want people on the team who approach projects with different perspectives and have diverse opinions. Depending on the project, consider adding people to the team temporarily from different parts of the organization. If you run your team solely with data scientists, you’re likely to lack a significant diversity of opinion. Team member backgrounds and training will be too similar. They’ll be more likely to quickly come to consensus and sing in a chorus of monotones.
I once worked with a graduate school that was trying to increase its graduation rate by looking at past data. The best idea came from a project manager who was an avid scuba diver. He looked at the demographic data and suggested that a buddy system (a common safety precaution in the world of scuba diving) might have a positive impact. No one could have planned his insight. It came from his life experience.
This form of creative discovery is much more common than most organizations realize. In fact, a report from the patent office suggests that almost half of all discoveries are the result of simple serendipity. The team was looking to solve one problem and then someone’s insight or experience led in an entirely new direction.
Frequently Asked Questions
What are the key roles in a data science team?
A data science team typically consists of data scientists, data engineers, machine learning engineers, and data analysts. These roles collaborate closely on data science projects, each bringing a unique skillset to ensure the project's success. Data scientists work on data analysis and model building, while data engineers manage the data pipeline and data transformation. Machine learning engineers focus on developing and deploying learning models.
How important is collaboration in a data science team?
Collaboration is important in a data science team. Effective data science team collaboration ensures that each member can contribute their expertise, leading to better solutions and more innovative results. A collaborative environment allows team members to work on overlapping tasks and exchange ideas, ultimately improving the overall data science workflow.
What are the main challenges of managing data science teams?
Managing data science teams involves coordinating diverse skill sets, balancing workloads, and ensuring effective communication among team members. Additionally, team leads must address challenges such as integrating new team members, managing data collection processes, and aligning the team’s goals with the organization's data-driven objectives. Providing clear direction and encouraging an open, collaborative culture can help your team overcome these challenges.
How should a data science team approach a new data science project?
When starting a new data science project, the team should begin with a clear understanding of the project's objectives and requirements. This involves comprehensive data collection, exploring the dataset, and developing a data pipeline to handle data transformation. The team should then collaborate on creating and testing machine learning models, ensuring they align with the project's goals.
What are some best practices for building a data science team?
Building a data science team requires careful consideration of the team's composition and skill sets. Team leads should prioritize diversity in expertise, including data engineers, machine learning engineers, and data scientists. Onboarding new team members effectively, providing continuous learning opportunities, and promoting a collaborative culture are also essential best practices to ensure the team's success.
How can software engineers contribute to a data science team?
Software engineers can significantly contribute to a data science team by developing and maintaining the infrastructure needed for data science processes. This includes building scalable data pipelines, integrating machine learning models into production systems, and ensuring the robustness and efficiency of data transformation tasks. Their expertise in coding and automation helps streamline the data science workflow and allows the team to focus on analytics and model building.
What skills in data science are essential for team members?
Essential skills in data science for team members include proficiency in data analysis tools, statistical methods, and machine learning techniques. Team members should also be skilled in handling and processing large datasets, data pipeline management, and data visualization. Additionally, strong communication skills are important for explaining complex results and collaborating effectively within the team.
What role does data pipeline management play in a data science team?
Data pipeline management is critical in a data science team as it ensures the smooth flow of data from collection to analysis. A well-designed data pipeline automates data transformation tasks, handles data storage efficiently, and provides data scientists with clean, structured data for analysis. Effective data pipeline management helps maintain data quality and accelerates the overall data science process.
How can team leads ensure their data science team is successful?
Team leads can ensure their data science team is successful by encouraging a collaborative environment, providing continuous professional development, and ensuring clear communication of project goals. Regularly reviewing the team's progress, offering constructive feedback, and encouraging innovative thinking are also key strategies. Additionally, team leads should align the team's efforts with the organization’s data-driven objectives to maximize impact.
What are the common phases of a data science project?
Common phases of a data science project include problem definition, data collection, data preprocessing, exploratory data analysis, model building, model evaluation, and deployment. Throughout these phases, data scientists work closely to ensure each step is executed effectively. Continuous collaboration and iteration are vital to refining models and achieving the desired outcomes.
This is my weekly newsletter that I call The Deep End because I want to go deeper than results you’ll see from searches or AI, incorporating insights from the history of data and data science. Each week I’ll go deep to explain a topic that’s relevant to people who work with technology. I’ll be posting about artificial intelligence, data science, and data ethics.?
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