07.18.2022 Executive Data Bytes – Who does what? – Data Roles and Responsibilities

Executive Data Bytes

Tech analysis for the busy executive.

Welcome to another edition of Executive Data Bytes! This week, we are discussing some interesting data science roles and their responsibilities.?

Focus piece: “A Deep Look Into 13 Data Scientist Roles and Their Responsibilities”

Executive Summary

A data scientist tells the story of the data by analyzing it and predicting what will happen in the future. There are many different roles within the field of data science, so if you’re searching for a job you should keep an open mind. In this article, KD Nuggets gives you an in-depth look at 13 data science roles and what they entail.

Key Takeaways

• Data scientists are asked to do a lot of different things by their employers, from finding areas of the business that could benefit from data analysis to making strategic decisions.
• A data scientist needs to be highly skilled in math, statistics, machine learning, visualizations, communication, and algorithm implementation. They must also thoroughly understand the business applications of their data.
• The data science field is young and loosely defined, and many companies have multiple data engineers or data analysts. These people all collect and analyze data, and then explain the results to non-technical colleagues.

Focus piece: “Top 9 Job Roles in the World of Data Science for 2022”

Executive Summary

Data science is a promising and in-demand career for skilled professionals. You need to have hard skill sets like analysis, machine learning, statistics, neural networks, etc. to start a successful career in data science. This blog by Great Learning will help you understand various career pathways and required skills and how to start a bright career in data science.

Key Takeaways

• A study by McKinsey Global Institute suggests that the US will face a shortage of close to 190,000 Data Scientists and 1.5 million managers and analysts.
• Machine learning engineers are in high demand today, but the job profile comes with its challenges. They must have in-depth knowledge of powerful technologies such as SQL, REST APIs, etc.
• Automation is less likely to take data science jobs if the right tools are developed, and more people with domain expertise can use data science to increase efficiency and enable objective decisions.
• Currently, a Data Scientist has one of the highest paying jobs in the industry. The future of Data Science is bright, and in the US, the average salary for a data scientist is US$122,525 per year according to Indeed.

Focus piece: “Distinguishing Data Roles: Engineers, Analysts, and Scientists”

Executive Summary

When a company gets serious about working with data, it brings in a variety of roles to collect, organize, and analyze the data. Some organizations create job titles that are counter to general expectations. This Chartio article describes general guidelines for differentiating between the three most prominent data roles in organizations: data engineers, data analysts, and data scientists.

Key Takeaways

• For a company that is just starting out, the most likely role is that of a data analyst. They are responsible for investigating data from multiple angles and finding trends, as well as identifying areas where more data could be collected.
• In a smaller organization, a data engineer might not be as high a priority because data collection products like Stitch and Blendo can handle the extraction of data from common data sources and cloud-based data storage solutions like Amazon Redshift and Google BigQuery. These programs are able to flexibly manage the manipulation of large amounts of data.
• The role of a data engineer has overlap with that of a data architect, but a data architect is responsibe for planning the architecture in which data will be processed and stored, while a data engineer manages implementation and maintenance.

Looking for Data Science Advisory? Look no further! Reach out today!

Who We Are

Data Products partners with organizations to deliver deep expertise in data science, data strategy, data literacy, machine learning, artificial intelligence, and analytics. Our focus is on educating clients on varying aspects of data and modern technology, building up analytics skills, data competencies, and optimization of their business operations.