Data scientist

Suggested CPD

Certificate in Data Analytics (CertDA)

The finance business partner

Certificate in Digital Innovation for Finance (CertDIF) 2021 Edition

What is a data scientist and what do they do?

This role will focus on the solving of complex business problems using machine learning, Natural Language Processing (NLP), and software development. 

This is done through the use of data wrangling and manipulation techniques (processing, cleansing and analysing structured and unstructured data to discover trends and patterns). Thereafter, the research and implementation of custom models and machine learning algorithms for classification, regression and for carrying out tasks associated with data quality, model explainability, among others, is carried out. 

To do this the data scientist will have worked with open source data science libraries and understand how to apply them to various problem types. They will also integrate technologies that provide an end-to-end solution, eg data collection to visualisation.

Key responsibilities

Responsibilities will vary, but examples include: 

• leading and delivering client solutions using a range of AI technologies
• research and development in support of new AI-based offerings
• learning about new technologies/research and how they could be applied for the benefit of our clients; development of innovative prototypes to bring to life our capability
• supporting business development via client conversations and presentations
• mentoring other experienced and aspiring data science and AI engineers
• speaking at conferences, meet-ups and other brand promotion events; blogging.

Why are they important?

The data scientist is instrumental in helping move organisations to transformational improvement such that they can lead in the market by becoming insight-driven.

Person specification

 

The data scientist will possess a detailed knowledge of a range of AI techniques, including graph data analytics, time series, NLP, deep learning, supervised and unsupervised machine learning, etc. Programming skills in Python or R, Spark and SQL are also required, as is experience of using the latest data science platforms (eg Databricks, AzureML) and frameworks (eg Tensorflow, MXNet, scikit-learn).

Previous professional experience in delivery of advanced analytics/DS/AI projects will be expected. 

To succeed, strong interpersonal, collaborative and team-player skills are essential, as are excellent communication skills, with the ability to analyse and clearly articulate complexities in a simple, clear and compelling way.