- Data Talent Pulse
- 4 Key Ways to Motivate Data Science Teams
4 Key Ways to Motivate Data Science Teams
In a previous issue, we had spoken to Ben Gowan of RiskLens, touching on the topic of keeping a team of Data Scientists motivated.
Building on these conversations, we delve deeper in this article to provide valuable insights for leaders striving to create a great environment for data scientists in their organizations.
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Leveraging inclusivity and community
Diverse teams tend to have employees who are highly engaged and motivated to deliver their best work.
According to research conducted by McKinsey, businesses that rank in the highest 25% for racial and ethnic diversity have a 35% greater chance of achieving financial returns surpassing the median in their respective national industries.
In addition, diverse and inclusive teams have been shown to -
As shown above, be more productive and have higher profitability.
Teams with a range of backgrounds offer a multitude of viewpoints and problem-solving methods.
Diverse teams are likely to consider a wider range of options and solutions, leading to more informed decisions.
Feeding the need for learning
More so than many other roles, continuous learning plays a major part in the work of data science.
Making sure your team is learning around the clock is a significant endeavour. Allow employees to dedicate a portion of their work hours to self-improvement and skill-building. Google's "20% time" policy famously allowed employees to spend one day a week on personal projects or learning, for example.
You will need to develop a comprehensive strategy for training your workforce and promoting the sharing of both business knowledge and data skills among teams. Here’s an outline of one way to go about it:
The infrastructure/The ecosystem
Efficient and motivated data science teams require a well-structured infrastructure, a set of essential tools, and a supportive ecosystem. Here's a brief overview:
High-Performance Computing: Powerful servers or cloud computing resources are essential for handling large datasets and running complex algorithms. For example, using AWS EC2 instances or Google Cloud AI Platform for scalable computing.
Data Storage: Robust data storage solutions, such as data warehouses (e.g., Amazon Redshift, Snowflake) or distributed file systems (e.g., Hadoop HDFS), are crucial for data retrieval and analysis.
Data Pipelines: Tools like Apache Airflow or Luigi can help automate data pipelines, ensuring data is collected, cleaned, and transformed consistently.
Scalable Databases: NoSQL databases like MongoDB and relational databases like PostgreSQL provide structured data storage.
Version Control: Git and platforms like GitHub or GitLab help maintain code version history and facilitate collaboration.
Training and Learning Resources: Access to online courses (e.g., Coursera, edX), conferences (e.g., NeurIPS, PyData), and internal knowledge sharing sessions can keep teams updated.
Supportive Leadership: Managers who understand the challenges and importance of data science foster motivation and innovation.
Cross-Functional Collaboration: Collaboration with domain experts, engineers, and business stakeholders ensures data projects align with organizational goals.
Clear Goals and Expectations: Setting well-defined project goals and milestones keeps teams motivated and focused.
Career growth that makes sense
Two ways to understand this:
1. Growth in compensation
Competitive Salaries: Ensure that compensation packages for data scientists are competitive with industry standards. Use data and benchmarks to determine appropriate salary levels.
Merit-Based Raises: Tie salary increases to individual performance and contributions. Recognize and reward exceptional work with bonuses or stock options.
2. Growth in role trajectory
Individualized Plans: Work with each data scientist to create a personalized development plan that aligns with their career goals and strengths. Offer support to achieve these goals.
Define Career Tracks: Establish distinct career paths for individual contributors and management roles. For instance, differentiate between Data Scientist I, II, and Senior Data Scientist roles, and Data Science Manager, Principal Data Scientist, etc.
Project Leadership: Allow data scientists to lead high-impact projects, even if they are junior. This provides opportunities to demonstrate leadership skills.
See you next time,
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