Data Scientists

Responsibilities for Data Scientist

  • Work with perspective clients or product owners to identify opportunities for leveraging company data to drive business solutions.

  • Mine and analyse data from company databases to drive optimisation and improvement of product development and business strategies.

  • Assess the effectiveness and accuracy of new data sources and data gathering techniques.

  • Develop custom data models and algorithms to apply to data sets.

  • Use predictive modelling to increase and optimise customer experiences, revenue generation, ad targeting and other business outcomes.

  • Coordinate with different functional teams to implement models and monitor outcomes.

  • Develop processes and tools to monitor and analyse model performance and data accuracy.

Qualifications for Data Scientist

  • Strong problem solving skills with an emphasis on product development.

  • Experience using statistical computer languages (R, Python, SQL, etc.) to manipulate data and draw insights from large data sets.

  • Experience working with and creating data architectures.

  • Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.

  • Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.

  • Excellent written and verbal communication skills for coordinating across teams.

  • A drive to learn and master new technologies and techniques.

  • We’re looking for someone with 5-7 years of experience manipulating data sets and building statistical models, has a Master’s or PHD in Statistics, Mathematics, Computer Science or another quantitative field, and is familiar with the following software/tools:

    • Coding knowledge and experience with several languages: C, C++, Java,

    • JavaScript, etc.

    • Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.

    • Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.

    • Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.

    • Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modelling, clustering, decision trees, neural networks, etc.

    • Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.

Brenda Lai