Data Scientist at Infinion Technologies

Job Description

• This hire is adept at using large data sets to find opportunities for process optimization and using models to test the effectiveness of different courses of actionincluding prediction.
• They must have strong experience using a variety of data analysis methods, building and implementing models, using/creating algorithms and creating/running simulations.
• To succeed in this datascienceposition,you should be highly analytical with a knack for analysis, math and statistics. Critical thinking and problem-solving skills are essential for interpreting data.

Responsibilities

• Assess theusefulness, 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.
• Developthe company’sframework 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.
• Identify valuable data sources
• Undertake preprocessing of structured and unstructured data
• Build predictive models and machine-learning algorithms
• Combine models through ensemble modeling.
• Present information using data visualization techniques.
• Proposesolutions and strategies to business challenges.

Requirements

• B.Sc / BA in Computer Science, Engineering or relevant field; graduate degree in Data Science or other quantitative fields is preferred.
• Minimum of 2 years work experience.
• Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
• Knowledge of Azure Data Service Ecosystem
• 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.