Data Scientist at Farmerline

As part of business growth and expansion, Farmerline is looking for a Data Scientist, who will be required to analyze information to predict patterns that will help to build data products to extract valuable insights for the business units in the company. The ideal candidate will support the sales and marketing units as well as the engineering and Farmer Services units with insights gained from analyzing company data to develop solutions and products, marketing strategies and techniques.

In this role, you should be highly analytical with an interest and expertise in analysis and statistics. Critical thinking and problem-solving skills are key to this role as business units will be developing strategies and plans based on your data analysis and insights.

Responsibilities

• Mine and analyze data from company databases to drive optimization and improvement of product development, marketing techniques and business strategies.
• Analyze industry information and data to discover trends and patterns to inform business decision-making.
• Work with the various units within the organization to develop algorithms to identify opportunities for leveraging company data to develop commercial products, and improve existing sales/marketing strategies and techniques.
• 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 optimize customer experiences, revenue generation and other business outcomes.
• Develop company testing framework and test model quality.
• Develop processes and tools to monitor and analyze model performance and data accuracy.

Required Qualifications

• 5+ years of professional experience manipulating data sets and building statistical models.
• Strong problem-solving skills with an emphasis on product development.
• Experience in using statistical computer languages to query databases (R, Python, SQL, etc.), manipulate data and draw insights from large data sets.
• Experience working with and creating data architectures.
• Ability to adjust and adapt to changes in role priorities and goals
• Working 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.
• Strong B2B, B2C / B2BC domain knowledge.
• Strong experience in e-Commerce, banking and agricultural operations.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.
• Geospatial data experience is a huge plus