These 5 Job Roles in Data Science Are in Great Demand. Are You Ready to Take Those Up?

 In CEO Corner

 

 

Data science has moved up the priority ladder and has become a strategic component for success for most organizations. Since moving away from its ‘buzzword’ status, data science professionals are now in high demand. Continuing with the upward trend, research shows that the data science job opportunities will continue to surge this year as well. IBM estimates an increase in demand for data scientists. According to the IBM research, there will be an increment of 364,000 to 2,720,000 openings for data scientists alone.

As the field of data science witnesses exponential growth, more job positions are getting formalized apart from the venerable role of the data scientist.

Here is a look at 5 roles in data science that are rising in prominence and are in great demand now.

1. Data Translator

The role of the Data Translator is rising in prominence and is soon expected to be as coveted as the role of the data scientist.

While organizations embark on their data science mission, they have to improve their dexterity to communicate data findings across departments. When doing so, they have to ensure nothing is lost in translation as the findings move from the data scientist to the decision-makers. According to McKinsey, only 18% of organizations are navigating this chasm effectively.

It is this reality that is forcing many organizations to look at the role of the Data Translator. A Data Translator acts as a conduit between the data scientists and the decision-makers. Since Data Translators are skilled at assessing business needs and are data-savvy as well, they can capably identify and define the right business use-cases that can be solved leveraging data. Along with having the capability to translate business problems to data problems, they also have to translate data-driven solutions to the language business-users can understand.

Domain expertise, business analysis, solution skills, and the desire to ask questions and challenge perceptions are venerable tools in the arsenal of the Data Translator.

2. Information Designer

Also called Data Artists and Data Designers, Information Designers, come from strong data visualization backgrounds. They are also adept at the grammar of graphics. As key players, their primary responsibility is to create the visual structure for insights communication. Almost the right hand of data scientists, information designers proficiently make comprehensive visualization charts and graphs and assist data scientists in designing interactive dashboard design models.

Information designers are responsible for creating the design for the information architecture and ensure the consumption of insights. For this, they have to have the capability to understand the user to drive adoption.

Proficiency in information design, user-centered design, and all aspects of visual design are essential skills that an Information Designer must possess.

3. Machine Learning Engineer

The role of the Machine Learning Engineer is being revered in the data science landscape. These engineers are sophisticated programmers who are adept at managing the mind-boggling amount of data that organizations have to play with.

Having a strong foundation in math and statistics, Machine Learning Engineers are entirely data and metric driven. They are responsible for running machine learning experiments and deploying machine learning solutions into production. Their primary role is to design and implement machine learning algorithms to address business challenges. These include the in-depth knowledge of applications and algorithms such as classification, prediction, clustering, anomaly detection, and the like.

Some of the key responsibilities of the Machine Learning Engineer are to build data pipelines to connect with the data. They also have to identify and build solutions to enhance performance and scalability and package the entire data science solution for use.

A software engineering background, data handling capabilities, a thorough understanding of data structures, algorithms, and object-oriented programming, and front-end and back-end coding dexterity are essential skills of a Machine Learning engineer. Along with this, they also should be adept at machine learning and deep learning techniques like clustering, decision trees, regression, and neural networks. Ability to mine structured and unstructured data and feature engineering are also core skills required to be a Machine Learning Engineer.

4. Data Engineer

Considered one of the hardest positions to fill, Data Engineers are now being considered as a core part of Data Science teams. These engineers hold the responsibility of collecting relevant data and then moving and transforming this data into ‘data pipelines’ for the use of the data science team.

The three key actions of the Data Engineers are to design, build and arrange these data pipelines. Data pipelines are essential to data science as these are “sequences of processing and analysis steps applied to data for a specific purpose. They’re useful in production projects, and they can also be useful if one expects to encounter the same type of business question in the future, to save on design time and coding” elucidates Colleen Farrelly, Data Scientist.

Data engineers have to use their logical and analytical capabilities to design the big data infrastructure and prepare it for analysis. Strong experience in Hadoop-based technologies, data warehousing, and NoSQL technologies are essential as is knowledge of general-purpose and high-level programming languages such as Python, R, SQL and Scala

Along with creating complex queries to build the data pipelines, they also have to arrange any and all problems into the programmed systems. For this, they need the skills to know what data to extract, have strong management and organization skills, and the dexterity to work with cross-functional teams.

5. Data Science Manager

The ability to do high-quality technical work comes from high-quality management. Once underrated, organizations are now looking at skilled Data Science Managers to lead their data science teams.

Since a lot of data science work is done in isolation – despite some data scientists working in teams, not all in the data science universe can lead teams. Data Science Managers are the ones responsible for leading data science teams. They chart the roadmap to help organizations mature in their data initiatives project after project. These managers are key stakeholders who drive the data culture in an organization and are responsible for identifying the project roadmap and scale the data maturity of the organization. They also ensure business value from data science.

For this, Data Science Managers have to focus on setting the right goals, metrics, and processes to identify, track, and measure impact. Along with this, they also have to drive Data Science projects, remove roadblocks that their teams encounter, provide frameworks to effectively prioritize work and establish the quality of the analysis. Data Science Managers have to work on driving product strategy and vision, promote ideas, and build team consensus.

Technical project managers might seem likely candidates to fit this role, but managers coming from software or business side functions can face significant challenges in understanding data science, data scientists and in defining the project work. We can consider data science projects to be hybrids of software engineering and consulting. Data science is also a highly academic field as compared to software engineering. It, therefore, becomes essential to have Data Science Managers who have –

·        Comprehensive understanding of the domain and algorithms

·        A clear understanding of the nature of projects

·        Capability to manage the team and the stakeholders

Finding someone who fits this trifecta is hard. It is no surprise that today Data Managers are becoming extremely valuable as an organizational resource in data science teams.

These roles might seem similar to ones we have heard of in the software development landscape. As the evolution of technologies happens, it is only but natural to experience an evolution of job roles and skill-sets. In some of our ensuing blogs, we intend to cover these transitions.

Article by Prashant Pansare

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