It is important for a data scientist to have solid programming skills, especially in Python or R, which are the standard for data analysis. Familiarity with libraries such as Pandas, NumPy, Scikit-learn for Python or Tidyverse, Caret for R is crucial for performing advanced data analysis and modelling.

The foundation of a data scientist's job is knowledge of statistics and machine learning. The candidate should demonstrate the ability to build, validate and implement predictive models and understand concepts such as supervision, unsupervised learning, classification, regression and neural networks.

Working with large data sets requires the ability to process and analyse them effectively. The candidate should have experience of working with big data using tools such as Hadoop, Spark, or cloud platforms (AWS, Google Cloud, Azure) to process large volumes of data.

The ability to present analytical results in an accessible and understandable way is essential. The candidate should be familiar with data visualisation tools such as Tableau, Power BI, Matplotlib, Seaborn to create clear visualisations and dashboards.

Effective application of data science to support business objectives requires the candidate to have an understanding of business processes and the ability to identify areas where data analytics can deliver the most value. Experience of working on business projects and the ability to communicate analytical results in a business context are key.

The data scientist must effectively communicate complex concepts and analysis results to team members, including non-technical people. The ability to work as part of a team, collaborating with other data scientists, developers, product managers and the business department to deliver data-driven projects is important.