Data Science Roles

Data Science Roles

Data science is all about extracting insight from data by utilising data analysis, machine learning, or advanced algorithms. Since data science is a fast growing and desirable area of work, you will find less traditional job roles are on offer with rapidly increasing demand. You may notice that many people working in the data science area have completed advanced degrees. Whilst this may be necessary for certain roles such as research, it is by no means a requirement. Many people transition between data science roles as their career and experience develops, for example, starting as a data analyst and becoming a data scientist.

Data Analyst

As we enter the decade of data, the ability to coax information and therefore insight out of data is becoming an ever more desirable skill set. And this is primarily what a data analyst does. There are a huge number of ways to utilise data but some of the most common are to create reporting dashboards, evaluate existing processes, and make recommendations to the business. As a data analyst, you may have some overlap with data scientists.

Key technologies to read-up on and practice: SQL, Python, Tableau, Alteryx, databases, Excel, data modelling, Spark

Data Scientist

Most data scientists spend their time working with machine learning algorithms and pipelines. As such, you’ll need to be confident in your mathematical and programming skills in order to really thrive in this role. You will have the chance to use many of the buzz words heard in technology nowadays; data mining, deep learning, synthetic data generation, to name just a few. Be prepared to get stuck in the detail of a process in order to ensure that the derived business insight is based on accurate operations and mathematics.

Key technologies to read-up on and practice: Python, Tensorflow, Keras, machine learning algorithms, statistics, databases, R, SQL

Researcher

As a researcher, you will be a pioneer in the world of artificial intelligence, machine learning, or statistics. Therefore an advanced mathematical degree will be a pre-requisite and a PhD comes highly recommended. You’ll more likely be aligned to an academic organisation of some kind than a profit-focused business, although collaboration with industry is commonplace. Research topics and techniques vary widely and there will certainly be emphasis on communicating you and your team’s developments, often in the form of publishing a scientific paper.

Key technologies to read-up on and practice: advanced mathematics and statistics, Python, machine learning algorithms, scientific method, knowledge of AI trends and techniques

AI Ethics

As AI makes the news increasingly more often, you may notice there is rapidly growing concern regarding the ethical implications of machine learning and the insight it yields. As an AI ethicist, you’ll need to have an excellent grasp of machine learning models and data standards in order to address often overlooked effects such as bias and transparency. Whilst these considerations may seem relatively trivial, they frequently culminate in outcomes that have a very direct and dangerous effects on people’s lives. As such, you’ll need to be both highly analytical and an excellent communicator.

Key technologies to read-up on and practice: machine learning algorithms, Python, AI trends, scientific method

Data Science not for you? Other Job Roles