Tips for Building a Successful Data Science Team: All You Need to Know

The Value of the Right Data Science Team 

Below are just a few reasons why a well-structured data science team is beneficial:

• Ensures that the right business problems are being identified and solved
• Appropriate data analysis can improve cost efficiencies and revenue, and target opportunities for business growth
• Algorithms can be effectively scaled in production 
• Data insights can be easily communicated to support leadership and empower business users
• Agile integration of various data sources
• Help improve decision making and infuse data and analytics into the DNA of the organization
• Understand market trends and corresponding impact on operations
• Helps to better link data insights to business actions
• Ensures that AI technologies can be efficiently scaled

Data Science Team Duties and Individual Roles

As a discipline, data science is a cross-functional, collaborative team sport where mathematical, statistical, analysis, and critical-thinking skills are deployed at an individual level to exploit collective skill sets and contribute to a high-performing, productive team culture. The team may use tools like Python, Hadoop, SQL, Tableau, R, and Tensorflow.

Data science team duties might include communicating the role of data to stakeholders, designing visualizations of data sets, comparing outcomes to corroborate accuracy, and determining trends by researching data sets, among other tasks.

On an individual level, roles and responsibilities fluctuate, but some core roles might include: 

Data Scientist 

Tasked with generating pertinent, actionable insights by analyzing and interpreting data via different advanced machine learning (ML), AI and statistical techniques. They also develop models and algorithms for mining and organizing data.

Data Analyst 

Often work with data that’s been standardized and transformed into a more accessible format, establishes that the collected data is pertinent, and may conduct specific types of analysis depending on the challenge. Additionally, they may create reports and visualizations.

Business Analyst

Fills the gap between organizations and IT departments, and deploy both analytics and business skills towards understanding how data-driven strategies can positively impact the bottom line across services, products, processes, hardware, and software. 

Machine Learning Engineer

Through a mixture of modeling and software engineering skills, they develop ML algorithms and models and figure out what data is best per model. They also train, monitor, and maintain the models, and automate data tasks by building AI programs.

Data Engineer

Creates a foundation of a database, including techniques for a solid architecture, implementation, performance testing, and continuous maintenance. Managing large-scale processing systems is also within their responsibilities, as is developing dataset protocols that streamline data mining, modeling and production.

Data Architect

Acting as a vital connection between technology and business, they craft a data strategy that extends to data flow, quality and security. They also transform business needs into technical requirements.

Additional Considerations

Organizations are adopting new technologies at an accelerated pace and replacing traditional analytics methods that are largely dependent on historical data with more advanced techniques. This often demands specialized skills and forward-facing data and analytics teams who can leverage a new class of analytics that depends on more diversified data. 

Structuring a data science team depends on a number of factors, from the size of the organization and how centralized its analytics initiatives are, to its overall data strategy, objectives and budget. 

Netflix, for example, is often acknowledged as an analytics pioneer and deploys numerous teams to perform different types of data analytics. Their approach to structuring analytics roles is to connect their analytics professionals with various business verticals, such as marketing, platform or product, instead of a functional horizontal. This enables their data science specialists, like Analytics and Visualization Engineers, to prioritize projects with the most impact, approach problems with more creativity, and not get stuck on specific roles or credentials. 

Looking forward to becoming a Data Scientist? Check out the Data Science Bootcamp and get certified today.

An Evolving Dynamic

The dynamics of a data science team will vary and responsibilities are going to evolve alongside developments and trends in analytics and new technologies. Prioritizing ongoing professional advancement and learning is a strategic asset for any data science professional, including those in leadership roles. 

Start your career in data science with Simplilearn's Data Science course and gain indepth knowledge of various aspects and core technology frameworks used for analyzing data.