Data Scientist

Data Scientist I identifies business trends and problems through complex big data analysis. Interprets results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently. Being a Data Scientist I designs, develops and implements the most valuable business solutions for the organization. Prepares big data, implements data models and develops database to support the business solutions. Additionally, Data Scientist I may require an advanced degree. Typically reports to a manager. The Data Scientist I work is closely managed. Works on projects/matters of limited complexity in a support role. To be a Data Scientist I typically requires 0-2 years of related experience.

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Data Scientist Job Description Template

Our company is looking for a Data Scientist to join our team.

Responsibilities:

  • Determine the suitability and feasibility of an analytical solution for a given commercial problem.

Requirements:

  • Exposure to engineering real time decision platforms and systems;
  • Predictive and financial modelling experience;
  • Preferred knowledge of text analytics/dealing with unstructured data;
  • Awareness of ethical and regulatory constraints in utilising various data sets;
  • Be excited to apply your data science knowledge to develop meaningful & predictive features for the wider analytics community;
  • Have a desire to work in an Agile environment – where we take it on, grow & value every voice;
  • Understanding the use of data science to support marketing initiatives;
  • Be flexible, adaptable to change and enjoy dynamic environments;
  • Experience in managing technical data analysis work;
  • Engineering principles such as data sourcing, cleansing, joining and ETL;
  • Bachelor’s Degree in quantitative (Engineering, Maths, Economics, Science or IT);
  • Data visualisation;
  • Machine learning (supervised and unsupervised);
  • Enjoy being hands-on and possess strong attention to detail;
  • Understanding of statistical techniques and approaches and the application of these for customer event analysis.