We live in the age of robust and fast-paced technological advancements. With each passing day more and more aspects of our lives directly intertwine with the digital approach. As our mindset as a society shifts towards trusting artificial intelligence more, the benefits it delivers to businesses are being recognised.
Digital transformation is now firmly established to be the path of future development of the business landscape. From startups to million-dollar companies, various sectors are thriving to adopt AI in aims of improving ROI and efficiency. However, they often forget it’s people that need to be invested in.
The key issue that arises is that growth of the AI’s capacities is quicker than the ability of the human skillset to adapt. It is being increasingly acknowledged that the stand-alone data scientist who is a master of all simply does not exist.
Although the numbers are grim, counting 80% of analytics projects fail, this leads to a firm conclusion. To be successful in harnessing the unlimited power of the modern AI you need a data science team of diverse skills as opposed to a ‘superstar’ data scientist.
In this article we are going to outline five key roles that need to be filled within a result-driven data science team. If you would like to discuss it with us in greater detail, please book an appointment.
Although for a successful project you need more than a Data Scientist, the position of one still needs to be filled. To briefly outline the role, Data Scientists collect and report on data which is crucial to shaping an organisation’s approach to business challenges. In the era of data overload, it is easy for businesses to miss potential revenue streams or areas their processes deliver low efficiency in.
The role of a Data Scientist is not to just collect and analyse data but to also use it for building models capable of explaining and/or predicting behaviour patterns. They must understand the link between the problems and the algorithms to them, as well as be capable of engineering the solutions. This is why it’s important for them to know programming languages such as R or Python.
In short, Data Scientists mine, process and visualise data to then process it in order of identifying trends which may uncover either problems or opportunities.
Data Analyst is a role more junior to a Data Scientist. As a rule, they are less advanced in programming languages but still fluent with usage of database skills and basic visualisation. Due to this fact their rates, as expected, are lower than what Data Scientists would charge. This in turn makes them a reasonably priced solution for companies that want to give data science a shot but are not willing to make a big investment.
Naturally, the question arises: why should you have both, Data Analyst and Data Scientist, when you can afford the latter, more senior one? Data Scientists usually pursue challenges, and many aspects of work Data Analysts take care of will not be offering them such – and when the work is not challenging enough, Data Scientists are likely to call it quits.
Alternatively, when faced with a big, time consuming challenge that requires high expertise and maximum concentration, Data Analysts make a perfect aide. In other words, a Data Analyst can, in a sense, be viewed as the Data Scientist’s assistant.
Occasionally a Data Translator is seen as a role more senior than a Data Scientist for their role is communicating the data to decision makers rather than collecting it. The job title is rather self-explanatory – the job of a Data Translator consists of creating a data story in a language that can be easily understood by people that cannot read data in its raw format.
Curiously, despite the vital importance of the role to the overall success of the team, Data Translators are usually seen as ‘nice-to-haves’. The reason behind most analytics projects failing is that companies compete to snatch the most talented data scientists as soon as they graduate to build sophisticated machine learning models. However, once that is done they end up with a lot of insights they can’t comprehend or put to use. Because there is no Data Translator – the key player behind delivering impact.
Data Translators are the ones that identify the trends and how they can be used towards achieving business specific goals. Coding and statistical knowledge are required for a successful Data Translator as they need to be able to understand the data they’re then relaying. However, the key skill is communication and an in-depth industry knowledge. Their key function is translating data into a language that can be understood outside the data science team.
Machine Learning Engineers often stay behind the scenes but their role is essential for digital transformation. Their job is to build and optimise data and data pipeline architecture – an infrastructure then used for further analysis. To put it shortly: Data Scientists design the algorithms and ML engineers then deploy and maintain them.
They are masters of integrating different tools and solutions, using their expertise in programming languages to create models and development operations for data-based solutions. Machine Learning Engineers take data from the Translators that’s been collected and analysed prior, embedding the patterns found within an algorithm that is then programmed into the solution. The top ML engineer will always go an extra mile ensuring the solution is stable and well-integrated.
Finally, the Data Science Manager is there to ensure the projects run as intended and everyone meets the requirements of their deliverables. Data Science Manager takes ownership of ensuring quality, setting up and tracking schedules and managing finances. Not only they are to ensure the delivery but also collaboration within the team.
Top Data Science Manager needs to be an excellent communicator and a project manager. Unlike hyper-focused Data Translators, they are generalists for all roles, making them the true jack of all trades. However, they specialise in data science project value identification and stakeholder management skills.
The success of how smooth communications run depends on their expertise, as well as client communications and the team’s ability to keep client commitments in timely quality deliveries.
As we have stated in the beginning – the Data Scientist capable of delivering on every skill set does not exist. This is why you need a Data Science team where each member has a set of specific skills they need to possess. There is one skill that needs to be present in every role, however: Data Science Literacy.
Passion and ability to work with data is what defines that skill the best and as the business scales it becomes vital for further development and success.
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