"Expectations Debt: Aligning Roles, Skills, and Org Structure for Successful Machine Learning Product Teams"

"Expectations Debt: Aligning Roles, Skills, and Org Structure for Successful Machine Learning Product Teams"

In the world of business, success is often measured by one's ability to meet or exceed expectations. When a company is thriving, employees are happy, promotions are abundant, and work feels meaningful. However, there is a concept known as "expectations debt" that can hinder the joy derived from such accomplishments. It is the idea that when we set unrealistic expectations for ourselves or our organizations, we create a debt that must be repaid before true satisfaction can be achieved.

Imagine a scenario where your investment portfolio skyrockets during a financial bubble. You feel a sense of euphoria as your wealth accumulates. However, the bubble eventually bursts, and reality sets in. Your portfolio crashes, and you are left with a debt to repay, both financially and emotionally. The same can be said for companies that experience a sudden surge in valuation or individuals who negotiate salaries that exceed their actual abilities. The initial thrill is short-lived, and the demands of reality catch up, requiring repayment with interest.

Now, let's shift our focus to the world of machine learning product teams. In this realm, there is a crucial need to productize and scale data cleanup and processing, which predominantly falls under backend tasks. Additionally, engineers collaborate with data scientists to ensure that models can scale effectively and that the quality of results in production meets the required standards.

When it comes to organizing machine learning product teams, there are three common approaches that can be taken:

Option 1: Data Science Reports to Engineering

By having data science report to engineering, there is a seamless alignment between the two disciplines. This approach eliminates the need for a clear distinction between data science and engineering skills. The team works together, leveraging their expertise to achieve common goals and deliverables.

Option 2: Data Science Reports to Product

Alternatively, having data science report to product ensures that the goals and deliverables of data science projects are driven by the needs of the product. This alignment guarantees that the work being done directly contributes to the success of the product. It also encourages collaboration and communication between the data science and product teams.

Option 3: Data Science Separate from Product and Engineering

In this scenario, the data science team operates independently from both product and engineering. This approach offers the benefit of providing visibility to the data science team and making it more accessible to the entire organization. It allows the team to focus solely on their data-driven projects without being influenced by the goals and priorities of other departments.

Regardless of the chosen option, it is essential to ensure that there is a clear and effective communication channel between all teams involved. Joint reporting often results in better alignment between teams, as it allows for a single decision-maker at the top. This person can provide guidance, resolve conflicts, and ensure that everyone is working towards a shared vision.

In conclusion, expectations debt is a concept that applies not only to personal achievements but also to the success of machine learning product teams. By setting realistic expectations and aligning roles, skills, and org structures, teams can avoid the pitfalls of unattainable goals. Here are three actionable pieces of advice to help navigate this process:

• 1. Set Realistic Expectations: Be honest with yourself and your team about what can realistically be achieved. Setting unattainable goals will only lead to disappointment and frustration. Celebrate small victories along the way to maintain motivation and momentum.

• 2. Foster Collaboration: Encourage open communication and collaboration between different teams and disciplines. By breaking down silos and promoting cross-functional work, you can leverage the diverse skills and perspectives within your organization to drive success.

• 3. Continuously Evaluate and Adjust: Regularly assess the effectiveness of your chosen org structure and make adjustments as needed. Flexibility is key in a rapidly evolving field like machine learning. Stay agile and adapt to changing circumstances to maximize efficiency and productivity.

By addressing expectations debt and aligning roles, skills, and org structure, machine learning product teams can create a culture of success and fulfillment. When everyone is working towards a shared vision and realistic goals, the joy derived from accomplishments is genuine and lasting.