Trends in Data Scientist you need to know

Data Scientist

Data scientists continue to be in demand in the world of work as more organisations complete their digital transformations and realise the power that their new data might hold. If you’re considering a career in data science,  keep these trends in mind.

First, What is a Data Scientist?

A data scientist helps organisations make the most of their data by unlocking the insights held in these vast swathes of unorganised numbers.

The impact that rapid digitisation is having on the data science sector is far-reaching and, as a result, these roles and skillsets are in high demand.

In its simplest form, a data scientist hunts through massive amounts of unstructured and structured data to provide insights and opportunities to help business’ meet specific needs and goals.

A data scientist wears many different hats. For example, the skills required as a data analyst, IT architect, test manager and data visualiser are all required under the data science umbrella. It can also be a lucrative career choice.

According to our 22/23 Hays Salary Guide, the average data scientist salary ranges between $120,000 to $180,000 across Australia and New Zealand, and the Bureau of Labour Statistics predicts that jobs in this field will grow by a further 11 per cent by 2024.

Clearly, with a skills shortage and attractive career prospects, a career in the data science sector is likely to be a profitable one. If you want to stand out from the crowd to capitalise on the opportunities a role in data science can offer, take these trends into account.

Future of data science

Data is driving energy customer experience

Data is driving every step of the customer journey and will continue to be a priority for many businesses.
Elements of the customer journey that many people gloss over, such as website chatbots, use data that has been analysed to give a better experience.
Many websites are now personalised to you in some way, depending on what you’ve looked at in the past, or how you’ve engaged with the website before – all achieved through insights gained from data collected.

Adaptive AI

Artificial intelligence is a subject that continues to be top-of-mind. With businesses constantly needing to be on their toes when it comes to decision making and transformation, Adaptive AI systems are entering the picture to speed up the decision-making process.

Increased data access

Breaking down barriers in sharing data is key in the future of data science. Being able to share data more freely, without risk across an organisation will enable more collaboration and businesses being more aligned when it comes to data analysis.

Data in the cloud

With all this data being generated and the volume only increasing, where is it all going to be stored? Storing data in the cloud allows for easy collection, analysis, structuring and even sharing. We will continue to see businesses start transferring their data to the cloud as part of their data management strategy.

Data scientist roles in Australia

Specialise in certain industries

Data scientist roles are not constrained to a single industry and have many skills that are transferable between sectors.
While almost all industries and sectors have a need for data scientists, organisations tend to lean towards data scientists that have some industry-specific experience.
Research your preferred sector and hone your skills to make your CV stand out to employers. For example, data security specialists are much sought after in the financial services sector, as the account and transaction data used in this industry is a high-value target to cyber criminals. For data scientists in the financial services industry, security and compliance, as well as fraud detection, are all favourable skills.

Cybersecurity continues to evolve

Cybersecurity continues to evolve in all industries and its relevance is only increasing. In this quickly evolving environment, it pays to have experience and qualifications in data security to assure prospective employers that their data is safe.

Balance robust academic achievements with on-the-job learning

While level of academic achievement, and the right technical skills are highly important in this category, there are also skillsets required to meet specific industry needs which can be obtained through attending professional development courses, online classes and bootcamps.
You may even want to take a more proactive approach and consider a big-data certification to really boost your CV.

Data analytics experience is essential, focus on machine learning trends

Data analyst roles are particularly in demand within the data science field. This is because businesses want to transform their data into insights that give a clear overview of their business.
Quantitative analysis is an important technical skill that allows you to analyse immense datasets. It will assist in improving your ability to run experimental analysis, scale data strategies and implement machine learning, which is another important piece of experience to help you stand out.
As a broad discipline, data science often overlaps with the machine learning sector, AI and deep learning. You may want to research these related disciplines further and borrow techniques from them to help better manage large unstructured datasets you often have to work with as a data scientist.

Make sure you have a solid business intelligence foundation

While data science is seen by many as the next evolution of business intelligence (BI), those working in this sector need to retain some basic BI skills. For example, communication is a critical soft skill you need to be able to describe the data you are working with and explain the analytics and insights you have extrapolated from that work.
Relaying complex technical information to non-technical professionals requires clear and effective communication and is a way to display the skill of your craft.
For your hard skillset, SQL programming skills show no signs of decreasing in popularity as a core method to manage data, and Tableau is a key BI tool for data visualisation that crosses over into the data science sector.

Keep your technical skills up to date

Don’t put all your eggs in one basket by focusing on just one technology or platform if you want to forge a career as a data scientist.
From a modelling perspective, SAS, R and Python are the common industry norms, and Apache Hadoop is emerging as the common framework. Many organisations are also turning to NoSQL, HBase and MongoDB databases to store large volumes of complex data.
Power BI, Teradata, ETL (both Informatica and SSIS) and IBM Db2 are all additional industry-leading tools in the data management sector that you should be aware of.
The complexity of data science means you need to demonstrate the most relevant skills and experience to this industry. If you can achieve this by proactively upskilling while simultaneously extending your experience, you will be rewarded with a lucrative and fulfilling career.

About this author

Adam Shapley, Managing Director, Hays New Zealand and Hays IT Australia & New Zealand, began working at Hays in 2001 and during this time has held significant leadership roles across the business including responsibility for multiple specialisms in various locations across Australia & New Zealand.

In 2018, he was appointed to Hays ANZ Management Board and made Managing Director for Hays New Zealand.

Adam is also responsible for the strategic direction of the Hays Information Technology business across Australia & New Zealand including driving growth across Digital Technology, Projects & Business Change and IT Operations & Support.

Follow Adam on LinkedIn

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