Managing and connecting data science talent and respective skills with data analytics projects is a common business challenge. But sustaining and scaling data science capabilities requires a complete range of skills in addition to understanding how specific roles can best support the business.
A comprehensive data science team can go a long way in establishing data science projects are unified, purposeful and productive. Data science skills are frequently structured around a specific project or business question, and when there’s an understanding of how certain roles and responsibilities align with business objectives, it can help team members deliver a more successful result.
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:
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.
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.
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.
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.
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.
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.
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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.
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