In 2012, data scientist was coined as the sexiest job of the twenty-first century by the Harvard Business Review. However, lots of companies still struggle to find and retain top data science talent, with one study showing that less than 2 percent of data scientists stay in the job for more than five years and the average tenure for a data scientist is only 1.5 years.
Team leads in companies where data science has just been incorporated can find it challenging to scale their teams. Without a defined plan, it’s often difficult to decide which use cases to work on and which direction to take the team. It’s also unclear which teams you should collaborate with, how to track success, and how to hire the right people.
If you’re trying to build or scale your data science team, this guide is for you. Here, you’ll learn what a team structure should be, how to fit its capabilities within your organization’s broader goals, and how to allocate resources to scale the team over time.
Achieving Data Science Success with a Small (Yet Mighty) Team
There is no one-size-fits-all answer to building a data science team. However, there are some common themes that emerge from successful data science leaders. Before diving into the guide, learn from data science leaders about how they have built their teams from the ground up.
Why You Need a Team-Building Plan
Data scientists sometimes struggle to find a holistic view of their business goals, especially when working remotely or within pockets of data science teams in different departments. This can lead to overly complex processes, slow onboarding of new staff, inefficient workflows, and unnecessarily duplicated efforts.
To lead a productive team, data science leads need to know and prioritize their team’s current workload, choose the correct use cases and tool sets, and provide sufficient system access to team members. For example, does the team have access to all the data required? Do they have accounts set up on databases and data warehouses? Do they have access to the compute and tools they need? Having a plan can help address these concerns as well as efficiently allocate resources and encourage collaboration.
Your team-building plan should cover the following:
- What your team structure will look like (ie centralized or decentralized)
- How you plan on integrating your team into the organization as a whole
- How you allocate resources
- How to scale over time
- How you plan to hire, engage, and retain top talent
In addition, such plans shouldn’t be rigid. The data science industry is rapidly evolving, and your planning process should adapt to these changes. Don’t be afraid to deviate from the plan, especially with large external changes to either your industry or your organization. Ensure your plan is flexible to adapt to both small (trying a new tool or framework) and larger (developing a CoE) changes.
Data science is a field where roles are still evolving and skills can significantly differ between companies. A team-building plan can help achieve a competitive advantage in the market by saving you from losing top talent to competitors. Your team-building strategy should define roles and responsibilities, onboarding processes, continuing education opportunities and collaboration techniques that keep your staff feeling motivated and enjoying their work.
Moreover, the success of your data science team relies partly on how well the team collaborates internally. Set up regular meetings, workshops, or even team-building events with teams like engineering, business development, or sales to grow relationships. Partnerships can prevent duplication of work, encourage knowledge-sharing, build skills, reuse artifacts from past projects, and equally distribute workloads. At the same time, such collaboration helps teams look at the overall business goals from a common perspective. Your plan should include the tools you will use to promote collaboration and a sense of belonging.
It can often take years to build a data science team with different specialties. Your plan should identify which areas require technical skills and how you can train new or existing staff to fill those voids. In this way, you build and retain knowledge and ensure the team is self-sufficient even when key resources are absent or leaving.
Data scientists are usually motivated individuals who look for exciting, cutting-edge technologies and projects. Your team’s morale will inevitably suffer if it’s always doing repetitive and low-skill tasks, getting pulled into different projects with competing priorities, or not completing projects due to organizational as well as external factors. This will lead to key team members walking away from the organization. You should, therefore, plan on how to retain team members by keeping them motivated with exciting projects, a clear definition of work, work-life balance, performance appraisal, and recognition.
In this article, you learned why having a plan to build your data science team is essential. You also learned which important roles you need to hire for, how to fit the data science team into your overall business, how to allocate resources efficiently, and how to scale your team over time.
Domino Data Lab offers an enterprise MLOps platform that can help you build and scale your data science team. It provides a self-service infrastructure portal for data scientists to quickly spin up development environments, a model factory to quickly test data science models, and a system of record that centralizes all artifacts from previous projects. Data scientists can find, reuse, reproduce, and build upon the saved components from past works. Version tracking enables avoiding conflicts when reusing work, and as technology changes, you can easily add or remove emerging tools.