Data Scientist

Organisations are generating and recording an ever-increasing amount of data in their operations and marketing activities. Data Scientists are a new breed of analytical data experts who interpret the data to get a clear understanding about how the company is performing.

Data Scientists combine mathematics with data and computer science and are experts in the world of big data and business intelligence. Mining through huge volumes of data to gather valuable business insights which can help improve productivity and boost revenues.

Role Description

The role involves fetching information from a wide variety of sources and uses programming skills, statistical modelling, and AI tools to analyse and get a clear understanding of specific business challenges and opportunities. It also requires critical strategic thinking to develop smart solutions that help the business make better decisions. A good Data Scientist combines technical, analytical, problem-solving, communication skills and strategic business sense with practical skills such as coding, mathematics and statistics.

You will have a curiosity to understand numbers and the insights that can be gained from them and have a passion for predicting trends and the use of data to inform business strategy and decision making. You will also have strong business sense, outstanding critical thinking skills and the ability to synthesise complex problems and use data to create solutions.

Main duties and responsibilities

As Data Analytics is a growing requirement in organisations, the role will contribute to developing a roadmap for its future scope and will work with others to strengthen its practice as a resource,for governance, and knowledge.

Some of the key responsibilities for this role include:

  • Review and assess the effectiveness of data sources and data-gathering techniques to improve data collection methods.
  • Process, cleanse and validate vast amounts of structured, unstructured and semi-structured data to gain valuable insights.
  • Utilise data-led insights to analyse the performance of operations and marketing. Develop clear reports and dashboards that facilitate business decision making.
  • Identify business problems and opportunities using machine learning tools and data science principles and methodologies to identify patterns, insights and opportunities.
  • Use insights to influence how an organisation approaches business challenges
  • Develop predictive models and machine-learning algorithms for consumers and the business. Use data analysis, dimensional modelling and data model design.
  • Guide and inspire the organisation about the business potential of ML and AI
  • Create a roadmap of key data science and enterprise BI capabilities and resources.
  • Develop reports and present insights using data visualisation techniques that propose solutions and strategies to business challenges.
  • Implement process improvement initiatives that drive improvements in metrics and insights.
  • Develop and maintain an adequate infrastructure and team to ease access of information to business data analyst.

Skills and experience

Most Data Scientists have a masters degree or equivalent in computer science, statistics, applied mathematics, big data or related fields such as economics, engineering or physics. You will have technical, mathematical and statistical expertise, and a natural curiosity and creative mind. You will also have experience in R, SAS, Alteryx, Power BI, DAX, SSIS, RDBMS, ETL, Solver, Python, Excel (advanced) and mathematical modelling with a specialism on machine learning, AI, data science or statistics.

You will have good understanding of data life cycles and technologies that support it including Microsoft Azure SQL, Datafactory, Databricks, and PowerBI. A knowledge of Java, C/C+, Perl and Ruby would also be useful. As would experience with Google Cloud Platform, SAP HANA and sound project experience in developing and implementing ML and data science to business outcomes.

Key skills needed to become a data scientist include:

  • Displaying a curiosity for improvements to business processes and innovation through Data Science.
  • Strong statistical skills including testing, distributions, regression, maximum likelihood eliminators etc.
  • A good working knowledge of machine learning methods such as nearest neighbors, naïve bayes, SVM and decision forests
  • Strong mathematical skills including multivariable calculus and linear algebra.
  • Proficient in handling imperfections in data.
  • Experienced in using data visualisation tools such as matplotlib, ggplt, d3.js and tableau to visually encode data.
  • Excellent verbal and written communication skills with the ability to engage with bot a technical and non-technical audience.
  • Critical thinking and problem-solving skills with an analytical and enquiring mind.
  • Expert in data science tools and methodologies.
  • Efficient decision making and organisational skills.
  • Ability to use collaborative programming tools like Git.
  • Ability to manage multiple projects and prioritise competing requirements.

Qualifications Required

There are routes into data science for both graduates and school leavers. As a graduate you will need a degree in maths, engineering, BIS, computer science or related discipline. School leavers can study a related topic at higher national diploma level or apply for an apprenticeship as a data analyst and progress from there. You will typically have at least 2 years data analysis experience and previous experience of ERP tools such as SAP and BI tools.

Did you know?
  • Between the dawn of time and 2003, five exabytes of data had been created at Google. By 2010, this amount of data was being created every two days, and by 2021 it was being created every 40 minutes.
  • There are approximately 400,000 bytes of data for every grain of sand on earth.
  • Companies that make use of customer analytics are 23 times more likely to outperform their competitors in customer acquisition (nine times for retention), according to McKinsey.

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