Data Science and Machine Learning jobs

Data Science has grown phenomenally over the last few years and with that, so have the variety of roles and responsibilities within the field. Sometimes, the creative names of job roles are hard to decipher, and it leaves us wondering exactly what our area of focus needs to be. This post explains some of the different areas of Data Science and the technical skills or knowledge you would typically need to be successful in the positions. Here are some of the common ones you would have come across.

Data Scientist

You will find that most people working in data are referred to as a Data Scientist, mainly because it is such a broad remit. We’ve even seen terms such as “Citizen Data Scientist” become popularised which simply means people who aren’t trained or qualified in the field but have taught themselves many of the applications.

A true Data Scientist is someone who can master several skills right through from working with raw data, using statistical techniques via data programming tools like Python or R and is able to present all those insights in a simple way to a wider business. The main functions tend to be machine learning, Big Data technology, predictive statistics, recommender systems and distributed computing (computers on numerous networks or disparate data). Big companies like Google and Facebook are famed for hiring many Data Scientists for their mass of complex algorithms that feed their businesses.

Data Analyst

A Data Analyst will take the work done by the Data Scientist and perform deeper analytics with it using platforms like R, Python, SQL, VBA (Excel). The focus of a Data Analyst is to work out why a number has happened rather than traditional roles which might have simply presented the results as management information. For example, if a retail business sells 10 products on a Monday and the previous Monday, they sold 20, a Data Analyst will work to find the root cause of the difference between the two days and feed that back in to the relevant teams.

Often, a Data Analyst will display their findings within reporting tools such as Qlik, Tableau or Sisense to name a few.

Data Engineer

A Data Engineer will be responsible for preparing the infrastructure that the Data Analysts and Scientists will be using. The models built by these roles are only as good as the data that feeds them, and a Data Engineer will ensure such governance of that via Big Data technologies, ETL processes and complex queries leading towards a “single source of truth.”

Data Engineers will not usually know machine learning or statistical methods but focus solely on the design and the architecture of the datasets. A great example of this is where businesses have several disparate data sources. Most probably have a website, a back-office system, finance system, telephony system, Google Analytics, Facebook Ads, Marketing platform, HR system, payments systems, email exchange, live chat or chat bots and perhaps even an App. The Data Engineer will work to get all of these into a single data warehouse that everybody else can use and gather trust in the work the rest of the team are doing.

Data Developer

This role is hot property right now and arguably the most sought after. A Data Developer is a bit like a mix of a Data Scientist and a Data Engineer. Where a Data Scientist is mainly focused on building statistical models and algorithms, the Data Developer will look to build products that turn these into full business solutions.

The Data Developer will gradually take data from different models, get some insights from teams and analysts whilst incrementally creating solutions that are able to deploy the work they are doing. So, ultimately, whilst the developer knows about engineering, machine learning and architecture, they look to create business value solutions using data. The exact context of this differs between businesses but it is a position where algorithms and machine learning can actually by implemented into business as usual processes.

Data Manager

A Data Manager will usually be an experienced data professional with a more strategic view of how data can be leveraged as a business asset for commercial use. They will understand all the different roles we’ve already spoken about, without necessarily being a master of any, in order to direct the team and promote a data culture both inside and outside of the organisation.

A role like this is incredibly important as Data Science projects are only useful if they have a practical or commercial purpose that provides a real-world benefit. For example, what if somebody in a Marketing team asked the Data Scientist to spend time building an algorithm that predicts which country the next customer will come from. It might be a nice fun little tool but serves no practical purpose to the business. A Data Manager will ensure that time is used effectively, and any projects are aligned to business revenue or customer experience.

What are Big Data and Machine Learning anyway?

All these roles fall into the world of Big Data and Machine Learning. These are both very broad terms that are subsets of artificial intelligence but they involve using machines to take data and automate tasks without the need for manual human intervention. Therefore, they tend to be used as buzzwords within most data roles, being so far reaching and applicable to virtually every project.

There are various other role titles coming out of the woodwork as the field of data continues to develop including Data Modeller, Data Architect, Data Evangelist, Data Artist or even a few Data Ninjas! However, all of these relate back to the core functions talked about in this article and they are the areas it is important to have a working knowledge of to progress in the field.