Top Skills Your New Data Scientist Needs

The world in 2021 needs skilled Data Scientists

At the root of Data Science is the need to use data in order to ascertain certain information and answer certain questions. Businesses that either possess data or are looking to collect data, need data scientists and those data scientists need a few different skills up their sleeves. We have put together a list of the top skills to look out for in the data scientists you are hiring in 2021.

Data Visualisation and Wrangling

If a company has mined or acquired data, which is not readily useable, it must be made useable. Raw data must be wrangled into a form that can then be understood, processed and modelled. This can be done by combining from multiple sources or cleansing the data of its mistakes and impurities. Once these actions are performed, it allows data professionals to analyse the data, confident that it is accurate. Figuring out what the data is telling them is another challenge that data scientists routinely face and visualisation helps two-fold. Firstly, as a means of them learning from the data and understanding what it has to tell them. Secondly, it helps them craft a narrative around, say, consumer behaviour, using visual graphics (with charts, maps, etc.) to communicate and convince the management.

Stats and Probabilities

The use of mathematic systems to decipher and decide what data to use and how to use it, is an important factor and your prospective candidate will need to be a maths whizz. This doesn’t just count for known numbers, but they must also be able to adequately estimate unknowns from knowns too. Statistics are usually based on probabilities so the two go hand in hand. Trends and dependencies can be identified using stats and probabilities which can aid in future planning for the business via data modelling. Patterns can be identified and problems can be predicted.

Cloud

An understanding of Cloud and Cloud Computing is incredibly important to data scientists in 2021, as it means they are able to access databases and tools on platforms from Google, IBM, Windows and Amazon. Access to huge amounts of data resources helps the data scientist mine, acquire, wrangle and analyse data much quicker than via traditional methods. Modelling and optimising performance are also made easier via the cloud, and savings in time, mean savings for your business.

Calculus

Multivariate calculus is an important skill within machine learning due to the use of unknowns and predictors. It is used in such areas as gradients or plotting and values for functions such as sigmoid, step, vector or cost. Matrix algebra and neural networks also come into play.

Software and Programming

Python is usually the major language you find data scientists working with, but of course there are many to choose from, including Java, Scala and SQL. Python is usually chosen as it is kind of a ‘one-size-fits-all’ contender. It will come as no surprise that programming is important within data as it covers a lot of skills which are integral to create useable data and deal with it.

Database

Yes of course that perfect candidate will have a lot of skills and one of the most important is the ability to define, store and index that data for ease of retrieval and use. Expertise within requesting and file structure will help your candidate cut through to what they need in order to test and manipulate it.

Curious about how Zenshin Talent can help your organisation hire the best candidates? Contact us today for a no-strings conversation about your needs and our experience.

Why Tech Startups Fail

Before reaching for the stars, startups must first do the groundwork

Amongst the biggest talking points we have noticed recently, is the ongoing curiosity surrounding start-ups and why they fail. If you have launched a start-up, are on the verge of launching one or are considering a business idea for the future, this list will definitely provide food for thought.

The National Association of Small Businesses conducted a survey and found that 51% of those surveyed believed that ‘the best way to learn more about entrepreneurship’ was to start a business. Now there is nothing wrong with taking the leap and starting a business, but certain factors must be in place before a business can become sustainable. 44% of such businesses do not make it beyond their fourth year. So why are they failing?

Obsolete Products

42% failed because there was no market need for the business, which comes down to lack of market research and ploughing ahead when signs may be indicating that it is not the right time, or it may never be the right time, for that idea. The problem with creating something revolutionary is that you must, not only create the product, but create the market for that product too. So many successful companies have succeeded by modifying or improving existing products. The next two largest reasons behind the failures were ‘running out of cash (29%)’ and ‘the wrong team (23%)’.

Money Management

Funding, or lack thereof, will always be the main downfall of a business and especially a start-up that must first disrupt a marketplace before offering the solution to the problem that is revealed. New tech, new systems, data projects, they all take time. Time costs money, and businesses also require people, who generally do not have the luxury of being able to work for free. Whether you are buying off-the-shelf products to aid your business’s development or you are having custom ones built, it all costs. From that first investment, through the testing process, if you run out of funds, all of that hard work was for nothing. CB Insights looked into 200 tech businesses that failed and found that these start-ups would implode within 2 years of reaching $1.3M funding.

Experience

Entrepreneurs looking to start a start-up will usually bring a skillset from their previous career and that can be greatly helpful but with the pressure to grow the business quickly, comes the pressure for all of the pieces of the puzzle to fall into place quickly. The best way for that to happen is for the requisite experience to be onboarded quickly and efficiently. If the business is lacking experience within key areas such as sales, marketing or data & AI, then it will be an uphill battle to succeed.

Growing Too Slowly

The prevailing desire amongst the founders of new start-ups is to create a company worth $1Bn to establish themselves as business geniuses. Entrepreneur Magazine found that the problem with this dream is that ‘Unicorns’ require the momentum to put them over the top or confidence in the business will dissipate, causing investors to look elsewhere while consumers will move on to the next big thing.

Lack of Strong Data/AI Team

Many start-ups are found within the tech space and, as opposed to already-existing or traditional businesses, start-ups are more likely to have included data science or AI within their business plan. This is a positive, yet problems can arise when teams assembled for data projects are unfit for the task or there is an imbalance within the team. The problem here is insufficient hiring practices, so a clear plan and experienced advisors are needed to bring in individuals with customer empathy plus adequate data and maths skills. External recruitment firms with experience in this field are integral to building these teams, saving time and money in the process. A partnership with retained services is generally best for both parties to be invested in the successful start-up.

Curious about how Zenshin Talent can help your organisation? Contact us today for a no-strings conversation about your needs and our experience.