Data Scientist

  • Basis:  Full-Time
  • Closing Date:  07 Dec, 2018
  • Job Ref:  KMP-38893

Job Description

Our client, a leading provider of performance marketing software is seeking to recruit a Data Scientist to be part of their data and analytics team!

The Job
Do you like coding? Our client, a leading provider of performance marketing software is seeking to recruit a data scientist to be part of their data and analytics team, to deliver world class data-driven products information about internal processes of the company. These products are aimed directly at improving clients’ bottom-line. Developing such products requires the best brains to discover valuable patterns and insights from the underlying data. The chosen candidate will report to the Head of Data and will be a key person in hunting for these insights.

Responsibilities:

  • Apply statistics, mathematics, machine learning, and predictive modelling to business requirements using various software tools;
  • Discover new information from the underlying data;
  • Ability to extract and compute the data according to business objectives;
  • Ability to visualise the insights through reporting and / or dashboarding;
  • Ability to operationalise Data Science experiments by creating, computing and calling web services;
  • Maintain the current Data Science processes to improve or change them.

Requirements:

  • Inquisitive skills – to explore data and ask “What if” and “What is” questions;
  • Research for new topics and models with other teams according to business requirements;
  • Proven experience in coding with Statistical or Machine Learning tools;
  • Proven experience in data analytics and statistics is a must;
  • Proven experience in visualising data, creating dashboards;
  • The ability to query databases;
  • A commercial mind-set with the ability to understand the business strategy.

Essential Skills:

  • Master / PhD in Maths, Statistics or AI field;
  • Knowledge of querying the data using SQL;
  • Knowledge of R/Python packages.

Desirable:

  • Knowledge of Financial, Quantitative Analysis;
  • Knowledge of the IT architecture of a Data Science work environment;
  • Knowledge of cluster-based approaches for studying data science like Hadoop, HIVE, PIG, SPARK;
  • Knowledge of Dundas or other visualisation tools.