Associate Data Scientist

The ideal candidate is adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action. They must have strong 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. They must have a proven ability to drive business results with their data-based insights. They must be comfortable working with a wide range of stakeholders and functional teams. The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

Your main duties in flying with us:

• 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.
• 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 impact on the business.
• Mentor and guide junior team members.
• Conduct research and development on novel machine learning techniques, including research paper writing and their applications.

Mandatory belongings that you must prepare:

• Master's degree in Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Minimum of 4 years of experience in data science, machine learning, data analysis and modelling or a relevant field.
• Proven track record of conducting research and publishing papers in reputable conferences or journals.
• Strong problem-solving skills with an emphasis on product development.
• Proficient in programming skills (Python, R, SQL, etc.) for data acquisition, processing, and analysis from large data sets.
• Knowledge of data science concepts (regression, classification, clustering, properties of distributions, statistical tests, and proper usage, etc.) and experience with applications.
• Understanding of designing and implementing statistical models.
• Knowledge of a variety of machine learning techniques (Random forest, SVM, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Excellent written and verbal communication skills for coordinating across teams.
• A drive to learn and master new technologies and techniques.