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and those authorized to work in the U.S. can be considered as W2 candidates.
Data Scientist
Location: Irving, TX
Duration: 12 Months
Description:
Act as Data Scientist on team that works on all the critical dashboards and automation tool for pulling customer feedback from digital surveys, chat, and voice to be able to help digital teams address major pain points proactively to effectively and quickly address customers biggest pain points and help to reduce significant costs and call volumes in operations. Analyze and understand data sources & APIs Design and Develop methods to connect & collect data from different data sources. Design and Develop methods to filter/cleanse the data Design and Develop SQL , Hive queries, APIs to extract data from the store. Work closely with data scientists to ensure the source data is aggregated and cleansed. Work with product managers to understand the business objectives. Work with cloud and data architects to define robust architecture in cloud setup pipelines and work flows. Work with DevOps to build automated data pipelines.
•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.
Brief Summary of Skills
•Data Scientist 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
•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.
•Must have 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.
•Familiarity with HTTP and invoking web-APIs Exposure to machine learning engineering
•Exposure to NLP and text processing
•Experience with pipelines, job scheduling and workflow management
•Experienced in managing work with distributed teams
•Experience working in SCRUM methodology
•Proven sense of high accountability and self-drive to take on and see through big challenges
•Confident, takes ownership, willingness to get the job done
•Excellent written and verbal communications and cross group collaboration skills
Competencies:
•Strong problem solving skills with an emphasis on product development.
•Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
•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.
•A drive to learn and master new technologies and techniques.
•5-7 years of experience manipulating data sets and building statistical models,
•Coding knowledge and experience with several languages: C, C, Java, JavaScript, etc.
•Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
•Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
•Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
•Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
•Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
•Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
•Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
