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Data Scientist Job Opportunity – Responsibilities and Requirements

A Data Scientist role is available for a professional who can help an organisation make better use of its data. The position focuses on analysing information, developing analytical and machine learning models, creating useful reports, and turning complex data into insights that business teams can understand and use.

The role would suit someone with a background in data science, statistics, computer science, or a related discipline. It is also suitable for an early-career professional who has already gained some practical experience in data analytics or data science and wants to develop further in an environment where data plays an important role in business decisions.

About the Role

The main focus of the position is to transform enterprise data into useful information that can support decision-making. Modern organisations collect large amounts of information from different sources, but raw data on its own does not necessarily provide clear answers. A Data Scientist helps analyse that information, identify patterns, and develop models that can help different teams understand what is happening and what may happen next.

The successful candidate will work with both structured and unstructured data. This means they should be comfortable dealing with information in different formats and using analytical techniques to identify useful findings.

One of the key expectations is the ability to turn analytical work into practical business insights. Rather than simply producing numbers or technical reports, the job holder will need to explain what the findings mean and how they may be relevant to the organisation.

Key Responsibilities

A major part of the position involves analysing data from across the organisation. The Data Scientist will work with business units to examine available information and identify patterns, trends, and other findings that can support business activities.

The role also involves developing predictive and prescriptive models. Predictive models can be used to estimate possible future outcomes based on available information, while prescriptive approaches can help explore potential actions based on analytical findings. These models can provide useful information for teams when making business decisions.

Another responsibility is developing and maintaining machine learning models. The successful candidate will need to understand how these models are built, tested, maintained, and used in practical applications.

Analytical pipelines will also form part of the job. These pipelines help move and process data so that it can be prepared for analysis and modelling. The candidate will therefore need to have a good technical understanding of how data moves through analytical processes.

Data Governance and Quality

The role is not limited to analysis and machine learning. The Data Scientist will also support the implementation of the organisation's wider data framework.

Data quality and governance are important because business decisions depend on the reliability of the information being used. The successful candidate will contribute to efforts aimed at maintaining data quality and following established governance standards.

An understanding of regulatory requirements is considered an advantage. This can be particularly useful when working with organisational data that may be subject to specific rules concerning how information is collected, processed, stored, or used.

Working With Data Platforms

The position requires experience or familiarity with modern data platforms. Microsoft Fabric and data lakes are among the technologies mentioned for the role.

The successful candidate will use data platforms to process and analyse information. This requires an understanding of how data can be stored, accessed, prepared, and used for analytical purposes.

Knowledge of cloud-based data environments such as Microsoft Fabric and Azure is therefore useful. Candidates who have already worked with cloud data platforms may find that their experience is relevant to the technical requirements of the position.

Dashboards and Data Visualisation

The Data Scientist will also develop dashboards and visualisations for stakeholders. Data visualisation is important because business users may not always have the technical background required to interpret complex datasets or analytical models.

A well-designed dashboard can present important information in a clearer format, allowing stakeholders to identify trends and monitor relevant business information.

The role therefore requires more than technical data skills. The candidate must also be able to communicate analytical results clearly and present information in a way that makes sense to people working in different parts of the organisation.

Working With Different Teams

Collaboration will be an important part of the position. The Data Scientist will work with stakeholders from both business and technology teams.

This means the successful candidate should be comfortable discussing technical work with colleagues who may have different levels of technical knowledge. They will need to understand business requirements, determine how data can help address those requirements, and communicate their findings clearly.

The ability to work with different stakeholders can also help the organisation identify new opportunities where data science can provide practical value.

Artificial Intelligence and Machine Learning

The organisation is also interested in identifying opportunities to use artificial intelligence and machine learning to improve business performance.

The Data Scientist will therefore be expected to look beyond existing analytical processes and consider where AI or machine learning could be applied effectively. This could involve identifying repetitive processes, analysing large datasets, improving forecasting, or supporting other business activities where advanced analytics may be useful.

The role provides an opportunity for someone interested in developing their experience in AI and machine learning while working on business-focused analytical projects.

Qualifications and Experience

Applicants should have a Bachelor's or Master's degree in Data Science, Statistics, Computer Science, or a related field. The academic background should provide the candidate with a foundation in data analysis, statistics, computing, or other relevant technical areas.

The position requires between one and two years of experience in data analytics or data science. It is therefore aimed at professionals who already have some practical exposure to working with data rather than someone entering the field without any experience.

Technical skills are also important. Candidates should be proficient in Python or R, SQL, and Power BI or similar data visualisation and analytics tools.

Experience with machine learning and statistical modelling is required. Candidates should understand the principles behind these techniques and have practical experience applying them to data.

Familiarity with cloud data platforms such as Microsoft Fabric or Azure is also expected. Knowledge of data governance and regulatory requirements would be an additional advantage.

Who Would Suit the Role?

This opportunity is suited to someone who enjoys working with data and is interested in solving business problems through analysis. The ideal candidate should be comfortable working with technical tools while also being able to explain results to non-technical stakeholders.

A strong interest in machine learning, data visualisation, cloud technologies, and artificial intelligence would also be useful given the range of responsibilities attached to the position.

Candidates should be prepared to work collaboratively, understand business requirements, analyse different types of information, and present their findings in a clear and useful way.

Overall, the role combines data analysis, machine learning, data governance, visualisation, and business engagement. It offers an opportunity for a data professional with one to two years of experience to apply their technical skills while gaining broader exposure to enterprise data strategy and advanced analytics.

Application

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