How many job roles can you count when you imagine a perfect Data Science team? The field of applied Data Science is highly interdisciplinary. As Data Science and Artificial Intelligence (AI) are becoming the prime solutions for any industry, the supply of top talent with these skill sets is becoming scarce.
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Organizations across the spectrum (be it startups or million-dollar enterprises) in several industries such as finance, retail, media, or healthcare, are realizing the benefits of AI.
In fact, 14 percent of global CIOs have already deployed AI, and 48 percent will deploy it by 2020, according to Gartner. Thus, a perfect and efficient data science team is required to handle multiple levels of tasks. However, building a team to successfully complete the AI projects is more complex than simply hiring data scientists and analysts.
A Data Scientist with a perfect blend of analysis, visualization, and storytelling skills is highly sought after. But the story takes a more drastic turn behind the scenes. Even if the teams are full of many data scientists, over 80% of analytics projects fail. And, the lack of a good Data Science team with diverse roles to handle multiple areas of a project is one of the prime reasons. It clearly means that a data science team needs more than just data scientists.
At Gramener, we diversify our Data Science teams with multiple roles to take care of distinct tasks. The reason is simple – A Data Scientist cannot handle everything that goes on in a Data Science team. Here are 5 job roles that we found are the most important to evolve the best Data Science team in your organization.
Read: 3 emerging Data Science roles you are missing in your team
To help you get the 360-degree view of each Data Science job role, we’ll highlight the responsibilities and skills of each role followed by the closest existing role it resembles.
Some Data Science experts consider the role of a data translator even bigger than that of a Data Scientist. Organizations are not fully able to grasp the relationship between data and business problems. A Data Translator is able to identify and define the right business use cases which can be solved using data. They have the ability to translate business problems into data problems. They can also translate the data-driven solution into a language that business users can understand.
| Responsibilities of Data Translator | 1. Own from inception to adoption 2. translate across domain & data 3. Act as a glue in the team |
| Skills of Data Translator | 1. Domain expertise 2. Business analysis & solutions 3. Interpersonal & mentoring skills |
| Closest role to Data Translator | 1. Business Analyst 2. Domain Expert |
Data Scientists have an analytical approach and most importantly, they are able to analyze data and identify important insights. They help solve complex business problems by identifying patterns in the data. They also have the ability to build models. It’s important to note that it’s enough if a Data Scientist knows fewer approaches (for example, linear and logistic regression) but knows where to apply these. This is better than knowing many approaches and not knowing where or when to use it depending on the situation. Data Scientists are also proficient in coding and using tools such as R or Python.
The role of Data Scientists is also challenging. Industries might need guidance from hiring experts on how to pick the right data scientist for the job.
| Responsibilities of Data Scientist | 1. Devise analytics approach 2. Analyze data & identify insights 3. Build ML models |
| Skills of Data Scientist | 1. Statistics and machine learning 2. Identify & interpret insights 3. Scripting skills (R, Python) |
| Closest role to Data Scientist | 1. Statistician 2. ML Expert |
Information Designers are also called Data Designers and Data Artists. They play a major role in Data Science teams as they create a visual structure for communicating insights easily. These people have a strong data visualization background and they’re well-versed in the grammar of graphics. They are not only proficient in making comprehensive data visualizations such as charts and graphs but also help the Data Scientists design models of interactive dashboards. Understanding how users perceive data is important. Again, mastery of all these skills is not required, but a strong grasp of design based on information is essential.
| Responsibilities of Information Designer | 1. Ensure the consumption of insights 2. Design information architecture 3. Understand user, drive adoption |
| Skills of Information Designer | 1. Information design 2. User-centered design 3. Aspects of visual design |
| Closest role to Information Designer | 1. UX Designer 2. Interaction designer |
Read: 3 Techniques for easy data consumption – Narratives, Visualizations, and Storytelling
These engineers play a huge role behind the scenes. They come from a software engineering background and are pretty good with coding and programming languages. They package all aspects of Data Science and play a key role in integration with other tools and solutions. Right from Running ML experiments using a programming language with ML libraries to deploying ML solutions into production, they handle it all. They build pipelines to connect with the data and are familiar with handling the data. With optimizing solutions for performance and scalability, they productionize models and handle development operations of solutions. Good ML engineers ensure that the solution is well-integrated and stable.
| Responsibilities of Machine Learning Engineer | 1. Package Data Science solution 2. Productionizing, DevOps 3. Data pipelines/integration |
| Skills of Machine Learning Engineer | 1. Software engineering 2. Data handling 3. Front-end/Back-end coding |
| Closest role to Machine Learning Engineer | 1. Software Engineer 2. Data Architect |
A role that is underemphasized in the industry today. Wrapped in the garb of a project manager, Data Science managers sit at the top of the Data Science pyramid and chart out the roadmap to help you mature the organization project after project. They are also the ones who ensure business value from the initiator. Data Science managers are the pioneers of adopting data as a culture in any organization. Their role is not limited to governance and they need to understand solutioning.
Plain vanilla project managers can’t just be plucked from other technical roles and be asked to manage Data Science teams — this could actually hurt the team more than doing any good because technical managers may not relate to several aspects of the Data Science team or the data solution.
Many Data Science projects have a probabilistic nature and are not very definitive. Data Science managers should be able to understand, relate to this and convince other stakeholders of this as well.
| Responsibilities of Data Science Manager | 1. Identify roadmap & scale maturity 2. Ensure business value from Data Science 3. Drive a culture of data |
| Skills of Data Science Manager | 1. Project management 2. Business analysis, solutioning 3. Team handling |
| Closest role to Data Science Manager | 1. Project Manager 2. Business Analyst |
Apart from job roles, there are other investments an organization makes in its data science journey. They need to ensure that they get maximum RoI on their investments. To give them a 360-degree view of the end-to-end data science cycle, we offer a wide range of data advisory services and workshops.
Gramener data science advisory workshops help organizations assess their data maturity to create data science roadmaps and define a clear strategy to implement data as a culture across every nook-and-corner of the business.
Every major job role in a Data Science team demands specific skills. While a Machine learning expert must be proficient with coding skills, a Data Science manager must have a flair for project management.
However, there is one skill you must always look for in every Data Science job role while building a team.
That skill is Data Science Literacy.
As your organizations start to cross stages of the data maturity model, the need for Data Science literacy increases. Being data literate means having a strong passion for data and number crunching. Data literacy puts a person in a comfortable position with data, measuring things in a quantitative manner. The prospect should be able to infer quick insights and patterns within the data.
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