Data Scientist

INVID-posted 4 months ago

Senior

11-50 employees

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A Data Scientist’s roles and responsibilities include extracting data from multiple sources, using machine learning tools to organize data, process, clean, and validate the data, analyze the data for information and patterns, develop prediction systems, present the data in a clear manner, and propose solutions and strategies.

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.

Experience querying databases and using statistical computer languages: R, Python, SQL.
Experience using web services: Redshift, S3, Spark, Digital Ocean.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks.
Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, and Facebook Insights.
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL.
Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot.
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.

Strong problem-solving skills with an emphasis on product development.
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) and experience with applications.
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master new technologies and techniques.
Coding knowledge and experience with several languages: C, C++, Java, and JavaScript.
Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, and social network analysis.

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