Leading Data Science Teams to Success

Data Science Consulting

Practical Strategies for Successful Project Delivery in Challenging Collaborative Environments

Introduction

Have you ever asked a team member for a deliverable and received something completely different? Or sent an email to a client project manager and received no response, even after multiple follow-ups?

Delivering products with a team can sometimes be challenging, and if you are the team leader, it is your responsibility to work through those challenges and still have a successful project. This can be particularly demanding in consulting data science projects, where collaboration with client resources is essential.

In addition, when delivering end-to-end data science solutions, the need for close integration with client infrastructure, access to data, and frequent stakeholder feedback means you will typically work with a cross-functional team. This team may include business analysts, data scientists, DevOps engineers, data engineers, project coordinators, and domain experts. Each member brings unique skills and perspectives, but they also come with their own sets of challenges.

The key to navigating these issues lies in an understanding of individual team members capabilities, weaknesses and strengths, proper planning and a focus on goal achievement. As the team leader, you must be agile and adapt your plans based on the strengths and weaknesses of your team, but also sometimes evaluate your own behavior, to see how your own actions are perceived. Below, I explore some common team challenges in data science projects and offer some key takeaways to ensure successful project delivery.

Challenges and Interventions

Leading data science projects involves overcoming a variety of challenges, from misunderstandings and lack of commercial insight to low productivity and disengaged stakeholders. Effective intervention often requires a combination of reflection, introspection, adaptive planning, one-on-one meetings and hands-on leadership. Here, I discuss specific challenges I have encountered and the strategies I employed to address them successfully.

Lack of understanding and underlying commercial insight

Challenge: On one of my earlier projects, I was put in charge of a couple of data scientists that were struggling to deliver on a project. They had built an algorithm that was giving the opposite answer to what they were expecting. In this case their model predicted that churn would decrease as price increased. The consulting partners on the project were not impressed, and needed a viable solution that the client could accept.

Intervention: My first course of action was to establish what we intuitively should expect from the algorithm. Then, I immersed myself in the data preparation and pipeline. (I totally support the common wisdom that data scientist spends 80% of their time cleaning and prepping data, and only 20% on generating insights.) This helped me discover that the issue was caused by how the data was being prepared and transformed from the source data, what data was included in the model and what other assumptions they made during the modelling process.

In addition to trying to lead by example and being hands on with the data, I also had frequent one-on-one meetings with the data scientist. These meetings let me clarify my expectations and better understand why they had gone down the wrong path with regards to modelling and data. Furthermore, I provided feedback on how we could course correct and get back on track. It was also important to constantly remind the data scientists about the end goal we were trying to achieve with the project.

Too academic without focus on the end goal of the project

Challenge: As is often common with data science and data engineering projects there are a lot smart people involved. Usually this is great, but sometimes good ideas can get in the way of delivering on your goal. I have often seen data scientists — and I myself have been guilty of this — delve too deep into a given problem and perhaps slightly loosing focus on what we are trying to achieve. This issue becomes extra acute in a hectic project setting, where we face budget, time and resource constraints.

On one occasion, I tasked a data scientist with developing a model for predicting churn, however he got stuck on the details and kept going down an analysis path that was not fruitful — despite not making significant gains. The data scientist was very rigorous in his approach and had difficulty taking a more pragmatic view of the situation. In this case we were also looking for a connection between churn and price, however the way the data was structured, and the algorithms that were being applied were not conducive to the end goal.

This challenge doesn’t only apply to team members, and as team leader you yourself can also easily fall into this trap. On a recent project, I was working a business case and was determined I had a great solution for approximating the ROI of our project. It was rigorous, bottom up and didn’t require any wild assumptions to be able to show our case. However, after working on it for a few days it turned out it was way too time consuming and complex for what we really needed. They client didn’t fully understand the methodology and it was clear we needed a simpler and more intuitive approach.

Intervention: In both cases, the solution was introspection and reflection on outcomes and use of time. Regarding the situation with the data scientist, I had one-on-one meetings with him to align on what we needed to achieve and deliver. What our end goal was. And for myself, by listening to feedback from my team and reflecting on my time consumption and progress, I was able to course correct and adjust the approach using a simpler technique that still met our objectives and allowed us to quantify the ROI of our project.

Low productivity team members

Challenge: Not all team members will be able to produce the expected output when initially planning a project. However, as a consultant, you are often required to make development plans that integrate client personnel — despite not being able to assess their productivity beforehand. Various factors, such as being overloaded with projects or dealing with personal issues, can contribute to lower productivity. Beware of making overly detailed plans without understanding the available resources, as this can be a trap.

On one project I was informed by the client they would provide we me 2 data scientist FTEs (full time equivalents), 1 business analyst FTE and one data engineer FTE that would help me deliver the project. Based on this I tried to plan out how we might be able to deliver the project within the given timeframe. However, when time came to deliver it turned out one of my data scientists FTEs had a couple of weeks planned leave, and there were no one to replace him. It also turned out that the data engineer FTE didn’t really deliver and wasn’t able to produce the level of output we needed for the project.

Intervention: The situation above was resolved by having short planning cycles and quickly adapting to changing conditions. I directed one of our more productive data science FTEs to pick up the slack and became very hands-on myself. This flexibility would have been difficult with longer, less adaptable plans.

Disengaged or uncooperative client side project manager

Challenge: In many consulting projects you will have a counter party on the client side that manages the project internally. This will typically be a person who either will own the end product you are building or someone whose role it is to manage projects internally and knows how to help the project move forwards.

Usually, it’s great to have someone on the client side who can contribute and help drive the project — especially when you need to align with multiple stakeholders. But sometimes you end up with a disengaged or uncooperative manager that doesn’t respond to emails and becomes more of a bottleneck than a help. For data science projects, where you ideally want frequent feedback, this can be problematic.

Intervention: Get regular meetings into their calendar. If you are used to weekly meetings with other managers, consider ramping that up to two meetings a week instead. Even if they don’t respond to emails, at least it is not long between in-person catch ups. If all else fails, another strategy can also be to use other channels to connect with the client. On one of my projects we had a consulting partner, who was not directly involved in the day-to-day operations of the project, reach out directly to the CMO on the client side and prompt the project manager to take more action.

Again, this is also a situation where reflection and introspection might be beneficial. Why is the project manager is behaving the way he is, is there anything about your own behavior that could have prompted it? Perhaps you did something or presented something in a way that rubbed off the wrong way? Try a one-on-one meeting to iron out any misunderstandings and align the focus on the end goal of the project.

Key Takeaways and Guiding Principles

This probably shouldn’t come as a huge surprise; it turns out many of the interventions we discussed above have a lot in common with the original principles underlying the agile software development movement. In the “12 Principles Behind the Agile Manifesto” we find a clear emphasis on a “preference to the shorter timescale… ”, and also ideas regarding reflection “…reflects on how to become more effective, then tunes and adjusts its behavior accordingly.” Additionally, there is also a focus on meeting and talking with people: “The most efficient and effective method of conveying information to and within a development team is face-to-face conversation.” Since agile was originally created as a method for software teams to improve their development processes in response to the prevailing waterfall methodology, it makes sense that many of its principles also apply to data science projects.

I have tried to consolidate my interventions into a few guiding principles, and of course some of the strategies discussed here will apply to many different types of projects, not just data science.

Planning with short iterations

If you rely too heavily on a predetermined large plan, you risk setting yourself up for failure. Especially when you don’t know the team very well. This is why having shorter development cycles with being able to adapt to quickly will increase your chances of success.

Keep the end goal in mind

This is a very strong guiding principle, and I often see it being a good way to approach problems and issues with team members. Especially when their efforts and analysis have drifted too far away from what we are trying to achieve.

In many of the larger data science projects I have been involved in, the end goal has been to implement some kind of end-to-end machine learning system into the client architecture. This could be anything from custom-made pricing solutions to a fully fledged customer management platform. For example, if you are working on a price sensitivity algorithm that is going into the end customer pricing function, frequently evaluate whether your efforts and research is really advancing the end goal of the project.

Lead by example

In most of my projects, I have had positive experiences with taking a hands-on approach and working in the details with team. Leading by example is effective because it builds trust and respect. When leaders demonstrate the behavior and work ethic they expect from their team members, it sets a clear standard and motivates others to follow suit.

Leading by example can be especially important when you have low productive team members. Also, it lets you be more flexible in your planning and it’s easier to jump in whenever parts of the development need more attention. Ultimately, I believe leading by example helps create a culture of integrity, collaboration, and mutual support.

One-to-one meetings

One-to-one meetings are particularly beneficial when leading data science projects due to the personalized communication and individual focus they provide. These meetings create a space for deeper understanding between managers and team members, allowing for tailored support and guidance on complex tasks. This is extra beneficial when you are a consultant and don’t know all the team members.

When you need to align with team members, one-to-one meetings offer a private space for constructive feedback, and ensure coordination between individual contributions and project goals.

Evaluate yourself and how you are perceived

Lastly, resolving issues and moving a team forward sometimes requires introspection from the team leader. Working with new people can be challenging, especially when coming from different countries and cultures. For example, I am from Norway, where direct and informal communication is common. This contrasts with the UK, where communication tends to be more subtle and polite.

I have personally experienced times when I was too focused on the end goal, assuming everyone was on the same page, without stopping to coordinate with the team. Additionally, I sometimes find myself becoming too technical when explaining difficult subjects. If team members don’t understand, they can feel excluded and frustrated, and you might come across as a know-it-all or arrogant. This is particularly challenging in data science, where complex topics often need to be explained quickly.

It’s Not All Doom and Gloom

While this article focused mainly on the challenging aspects of working in a data science team, there are of course many aspects of it that are amazing, and why I continue to do it. I have met some really smart and engaging people who have contributed to my data science journey and taught me invaluable skills. In addition, together as teams we have been able to deliver way more than any of one of us could have done.

I would not be without those experiences, and I am grateful for the opportunity to be able to work on a diverse set of projects, interesting people and data challenges. (I even met my future wife and the mother of two of my boys on a project!!)

Conclusion

Leading data science teams comes with its unique set of challenges. From dealing with varying productivity levels and ensuring alignment with client resources, to maintaining focus on the end goals and fostering effective communication, the role demands a versatile and adaptive approach. By maintaining short planning cycles, leading by example, and utilizing one-on-one meetings for personalized guidance, team leaders can steer through these challenges more effectively.

While obstacles may be numerous, the rewards of working with a great team to solve complex problems are substantial. The experiences enhance professional growth and contribute to personal development and meaningful connections. Embrace the challenges, learn from each project, and continue to grow as a leader!

If you enjoyed reading this article and would like to access more content from me please feel free to connect with me on LinkedIn at https://www.linkedin.com/in/hans-christian-ekne-1760a259/ or visit my webpage at https://www.ekneconsulting.com/ to explore some of the services I offer. Don’t hesitate to reach out via email at [email protected]

Leading Data Science Teams to Success was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.