A business intelligence analyst, also known as a BI analyst, uses data and other information to help organizations make sound business decisions, including: Breaking down key business data, Interpreting the data and Sharing findings.

Data engineering is the practice designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure the data is in a highly usable state by the time it reaches the data scientists and analysts.

Data scientists determine the questions their team should be asking and figure out how to answer those questions using data. They often develop predictive models for theorizing and forecasting. Including: Find patterns and trends in datasets to uncover insights; Create algorithms and data models to forecast outcomes; Use machine learning techniques to improve the quality of data or product offerings; Communicate recommendations to other teams and senior staff; Stay on top of innovations in the data science field.

An enterprise architect is responsible for the upkeep and maintenance of an organization’s IT networks and services. As an enterprise architect, you will be responsible for overseeing, improving and upgrading enterprise services, software and hardware. You will also need to stay on top of the latest trends and technologies and keep an eye out for any software, services or hardware that might improve business processes.

Machine learning scientists often have the word ‘Research’ in their title as well. This means that you may focus more on researching algorithms in general before implementing a more simple approach.

A Cloud Infrastructure Architect is an IT professional who supervises an organization’s cloud computing ecosystem. This includes cloud adoption, cloud application design, and cloud management and monitoring. Cloud architects manage application architecture and its deployment in a cloud-based environment. This includes the public cloud, the private cloud, and the hybrid cloud.

Data Modelers are Systems Analysts who design computer databases that translate complex business data into usable computer systems. Data Modelers work with data architects to design databases that meet organizational needs using conceptual, logical, and physical data models. Their models are designed to improve efficiency and outputs, and may focus on issues such as reducing data redundancy or improving data movement across systems.

The job of a data architect is to develop, analyze, and administer data, data systems, computer systems, and anti-hacking software for organizations. Data architects use their sophisticated design and development skills to collect data and translate it for use in computer applications and systems.

Machine Learning Engineers are highly skilled programmers who develop artificial intelligence (AI) systems that use large data sets to research, develop, and generate algorithms that can learn and make predictions. Overall, this role is responsible for designing machine learning systems, which involves assessing and organizing data, executing tests and experiments, and generally monitoring and optimizing machine learning processes to help develop strong performing machine learning systems.

A research analyst is responsible for researching, analyzing, interpreting, and presenting data related to markets, operations, finance/accounting, economics, customers, and other information related to the field they work in. A research analyst is typically very quantitative, analytical, logical, and good at managing numbers and data. This guide will break down the main aspects of being an analyst in different industries, with a focus on the finance industry.