Data science is a multidisciplinary field combining technical skills across mathematics, statistics, computing and information to tackle real-world problems.
Businesses receive swathes of information about the world, but rather than being in some natural, readable form, this is often hidden in terabytes, sometimes petabytes, of data. It is the role of a data scientist to interpret and tease this valuable information from the data available, in any and all of its forms.
This information can be anything from the “what” or “why" something happened, to the “what will” or “what could” happen. Often, this requires bringing to bear many of the tools, techniques and creativity of data science.
Data science allows a business to ask questions of their data - the richest vein of real-world information - and get meaningful and actionable answers back. These can in turn be used to make real, data-driven decisions.
This is why we've created Data-science-as-a-service.
Our end-to-end service and product suites are underpinned by our data-science-as-a-service team.
Our data scientists draw upon domain knowledge and mathematical and statistical expertise to find value in data. These brains will often find and deliver this value through advanced analytics and machine learning models. Their key skills include: Maths and stats, data cleaning, visualisation, communication, and business understanding.
Machine Learning Engineer:
Our machine learning experts provide a similar function to the data scientist, with more focus on operationalising and optimising the fruits of data science as a real world application. They blend both deep data science with data engineering to ensure the theory is on the same page as the implementation. Their key skills include: machine learning, analytics, data cleaning, visualisation, systems, programming and distributed computing.
Data + Implementation Engineer:
These smarts predominantly support with the design and implementation of systems that need to handle big data. With their advanced programming skills and appreciation of the nature of data, they can ensure resilience and availability of data-driven software solutions. Key skills include: data pipelines, databases, distributed computing, programming, design principles and APIs.
The cherry on the top is our engineers who obtain the most advanced programming and system design knowledge, often supporting data engineers. Deeply versed in different technologies, code design principles, different hardware, etc. They create software and tools that work, and work well.
Key skills include: programming, software best practices, testing, UI, API, deployment.