Data Scientist

In Daufood, we are looking for a Data Scientist to join our Business & Market Insight Department, to support our leadership team as well as the business areas such as Operations and Marketing teams with insights gained from analyzing company data.

The ideal candidate is adept at large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action.

The primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our products.

He/She must have experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations.

He/She must be comfortable working with a wide range of stakeholders and functional teams.

We are looking for somebody passionate for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Responsibilities:

• Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.


• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques 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 modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.


• Develop company A/B testing framework and test model quality.


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


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


• Extending company’s data with third party sources of information when needed


• Enhancing data collection procedures to include information that is relevant for building analytic systems


• Processing, cleansing, and verifying the integrity of data used for analysis


• Doing ad-hoc analysis and presenting results in a clear manner


• Creating automated anomaly detection systems and constant tracking of its performance