Digital transformation has taken over various industry verticals and is steadily accelerating. More businesses are recognising data to be an insight-generating tool of a high value, capable of supporting improval of various internal and external processes. From product development and customer retention to identifying new revenue streams and dodging threats, data science allows businesses to sustain, grow, and stay a step ahead of the competition.
The value data adds to business is enormous. In a modern day, when data is incoming from multiple sources and in different formats, organisations are presented with multiple opportunities to build their strategies based on it. However, a variety of skills are needed to take advantage of data – that’s why companies invest in data science teams. Today we will be talking about data scientists and where they fit within the team.
For projects to deliver impact, data needs to be cleansed, digested and translated. Data scientists are the core of process optimisation, market segmentation, innovation and, ultimately, growth. This position requires a unique blend of skills as being a data scientist means being both numbers and commercially driven, with a customer-focused mindset.
In this article, we will outline 6 key skills every success-driving Data Scientist should possess. If you’d like to know more, we are happy to book a call.
Being fluent in a programming language such as R or Python is fundamental and the primary requirement for a data scientist. This is the key to applying algorithms to data to gather insights and drive efficiency.
Being fluent in programming languages allows data scientists to write code that can be reused instead of having to reproduce the same analysis manually every time. A well-written code enables automation which in turn allows data scientists to easily move on to other projects and keep adding incremental value.
Programming language is the core of data science skills such as working with machine learning models, aggregating data and running hypothesis tests.
It is vital for a data scientist to have a good understanding of statistics. Ability to read and understand statistics may seem like an obvious skill to have, which leads to it often being taken for granted.
It allows data scientists to choose the right algorithm and approach. Programming skills enable automation of analytics, however statistics is the key to having the right analytics. With no statistics-based optimisation, automation won’t solve the problems: it is likely to create more.
Data Scientists need to possess this skill to identify relationships or dependencies between variables, predict future trends, make forecasts based on previous data trends, spot anomalies and determine patterns.
Most often the data a business acquires is messy, incomplete and overall not ready for modeling. Being able to understand and work with imperfections in data is one of the key skills needed for a data scientist.
Missing values, inconsistent date and string formatting and other small bits can make data difficult to read. This is where data wrangling becomes important – it is a process of transforming and mapping raw data from one format to another, making it insights-ready.
Simply put, it is the process of cleansing the data that gives accurate insights on actionable data on hand. It also reduces time collecting, processing and organising data before it can be utilised. Not to mention, hiring someone who can automate the process instead of someone to do it manually eliminates the factor of human error. Clean and accurate data leads to accurate, data-driven decision making.
Machine learning is a big chunk of digital transformation and will likely play an ever increasingly important part going forward. Therefore it is a skill you want your data scientist to have. Curiously, a large number of data scientists are not proficient in this area even when they claim to be. When interviewing candidates, test them by asking to explain some of their favourite machine learning algorithms in simple terms. Additionally, ask when it is appropriate to use certain algorithms. This is a good practice to determine whether the candidate is above average.
ML is mostly the process of training a program to make good decisions by getting it to learn from past examples. It means that, unlike traditional software programming, it doesn’t require explicit commands in order to carry out operations with repetitive patterns. Precise machine learning models help with forecasts, thereby identifying profitable opportunities and avoiding potential risks.
Having technical skills for collecting, processing and analysing data is not enough for an impact-driving data scientist. On top of their job specification knowledge, they must also have a solid understanding of the industry they are working in. Otherwise, they will not be able to identify problems your business is trying to solve nor prioritise the most important ones first.
To understand how solving a problem will impact the business the data scientist must understand internal processes, mission and even culture. This way they can tailor the most effective data-driven approach that aligns with how the business operates, ensuring the efforts are applied in the right direction. This is particularly important when building a new data science team. How will they guide you without understanding your industry?
Finally, the ability to clearly communicate technical findings to a non-technical team is an absolute must for a successful candidate. However, you should manage your expectations. Most data scientists are not primarily great at this: it is a skill to be developed, so rather than expecting them to be great at it straight away, be ready to invest in training.
A data scientist is a person to enable the business to make decisions based on insights. Their job is to build a storyline around the data, making it easy to understand for both the stakeholders and representatives of different departments.
It is important that the delivery of insights is focused on how the findings impact the business rather than what has been analysed. Data scientists should be aiming to deliver the value embedded in data rather than explaining the meaning behind the figures.
Data scientist is one of the key players on the data science team – but not a sole member of it. The job demands both technical and commercial skills, although the genuine passion for data science is what lays at the core of it.
When hiring a data scientist don’t limit yourself to ticking technical skills boxes only. A star data scientist is the one who uses it as an aid and not a solution to tackle business challenges in the most efficient way.
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