Eps 4: Facebook's Jason Sundram - How to Build Data Science Teams for AI Projects
In the 10-minute podcast, Facebook's Jason Sundram provides advice on building data science teams for AI projects. One important tip is to ensure that team members possess a strong foundation in statistics and programming languages. Collaboration and communication skills are also essential for team success. Sundram encourages a multifaceted approach to problem-solving, including continuous iteration and experimentation. Additionally, he emphasizes the importance of clear goals and objectives and maintaining a strong connection to business outcomes. Ultimately, building strong data science teams requires a thoughtful and strategic approach to hiring and team development.
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Host
Crystal Barnes
Podcast Content
Jason Sundram is currently the Director of Data Science at Facebook where he leads a team of data scientists responsible for developing and deploying AI-driven solutions across Facebook's products and services. He has a wealth of experience in data science and has worked in various companies including eBay and Netflix.
So let's get started. Jason, we know that building a data science team for an AI project can be a challenging task. What according to you are the key factors in building a successful data science team for AI projects?
Jason: Yes, building a data science team for an AI project is a challenging task. The success of any AI project relies heavily on the quality and competence of the data science team. Therefore, it's essential to get the team composition right, ensuring that it has the necessary skills and expertise required to deliver the project successfully.
Firstly, finding individuals with the right technical skills is paramount. These are people who possess programming skills, statistical knowledge, machine learning algorithms, and domain knowledge. They are people who can process, analyze, model, and interpret data.
Secondly, communication skills are also crucial. Data scientists need to communicate effectively with colleagues, clients, and stakeholders, and this is especially important in cross-functional teams that work on AI projects. It's essential to have data scientists who can explain complex statistical concepts to non-technical stakeholders effectively.
Thirdly, it's important to have individuals who constantly strive towards self-improvement, are passionate about data science, and have a natural curiosity. This is because they will be dealing with new and complex problems all the time, and a curious and passionate mindset will help them navigate most of these problems effectively.
These are the critical factors that need to be considered when building a successful data science team for an AI project.
That's interesting Jason. But, in addition to these key factors, what are the challenges that organizations face while building data science teams for AI projects?
Jason: Yes, there are several challenges that organizations face when building data science teams for AI projects. One of the biggest challenges is finding the right talent. Data science talent is scarce, and the top talent is often in high demand. Employers need to differentiate themselves from others in the industry in order to attract and retain top talent. This includes creating an environment that fosters creativity, innovation, career growth, and professional development.
Secondly, integrating data science teams with other departments is also a challenge. Often, data science teams are siloed from other departments and do not have full access to the data they require. This can make it difficult for data scientists to provide actionable and impactful insights and recommendations. Integrating data science teams with other departments and having open communication channels helps to break down silos and leads to enhanced collaboration.
Finally, managing expectations is a challenge when working on AI projects. There is often an expectation that AI projects will deliver tangible results quickly. However, building and deploying AI systems is a complex and iterative process that requires extensive testing and validation. Managing those expectations and delivering projects that are functional and impactful in the long term requires a shared vision and understanding between all stakeholders involved in the project.
These are some of the critical challenges faced by organizations when building data science teams for AI projects.
Thank you, Jason, for sharing your insights on building data science teams for AI projects. Do you have any final thoughts that you would like to share with our listeners?
Jason: Yes, I would like to emphasize that building data science teams for AI projects is crucial to achieving success in AI-driven solutions. Organizations need to find the right talent, integrate data science teams with other departments, and manage expectations effectively. They also need to invest in the latest technologies, such as cloud computing, which enable data scientists to leverage large datasets more efficiently and enable faster iteration cycles.
Building a data science team is a long-term investment that requires patience, perseverance, and a shared vision. It is only through a well-constructed, competent, and integrated data science team that organizations can achieve tangible and impactful AI results.
Thank you once again, Jason, for sharing your valuable insights on this topic. It was a pleasure to have you on our podcast. For our listeners, don't forget to tune in to our next episode where we will explore the latest AI trends and innovations in the industry.