Dice
Data Scientist
Dice, Charlotte, North Carolina, United States, 28245
Dice is the leading career destination for tech experts at every stage of their careers. Our client, Zealogics, is seeking the following. Apply via Dice today!
Responsibilities:
Work with stakeholders, identifying opportunities for leveraging data to solve for business challenges.Identify valuable data sources / data sets that can be leveraged to improve results.Analyze data to interpret against business opportunity and discover trends and patterns.Process, cleanse, and verify the integrity of structured / unstructured data used for analysis.Research and implement custom statistical models and machine learning algorithms.Execute analytical experiments methodically to evolve an idea into successful solution.Coordinate with engineering and software development team to integrate model into continuous business / process / software cycle.Present information using data visualization techniques.Communicate results and ideas to key stakeholders / decision makers.
Qualifications:
Masters Degree in Computer Science, Statistics, Applied Math or relevant field7+ years' practical experience as a Data Scientist with proven track recordStrong math skills (e.g. statistics, algebra, multi-variable calculus)Expertise with R, SQL and Python; familiarity with Scala, Java or C++ is an assetExtensive background in data mining and statistical analysisDeep understanding of real-life applicability and limitations of machine-learning algorithmsProblem-solving aptitudeAnalytical mind and business acumenExcellent communication and presentation skills.
Skills and Experience:
Experience with B2B, Financial Industry, Asset Management, Sales & Marketing is highly desired.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.Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.Expertise querying Relational / No-SQL databases and using statistical programming languages like R, Python, etc.
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Responsibilities:
Work with stakeholders, identifying opportunities for leveraging data to solve for business challenges.Identify valuable data sources / data sets that can be leveraged to improve results.Analyze data to interpret against business opportunity and discover trends and patterns.Process, cleanse, and verify the integrity of structured / unstructured data used for analysis.Research and implement custom statistical models and machine learning algorithms.Execute analytical experiments methodically to evolve an idea into successful solution.Coordinate with engineering and software development team to integrate model into continuous business / process / software cycle.Present information using data visualization techniques.Communicate results and ideas to key stakeholders / decision makers.
Qualifications:
Masters Degree in Computer Science, Statistics, Applied Math or relevant field7+ years' practical experience as a Data Scientist with proven track recordStrong math skills (e.g. statistics, algebra, multi-variable calculus)Expertise with R, SQL and Python; familiarity with Scala, Java or C++ is an assetExtensive background in data mining and statistical analysisDeep understanding of real-life applicability and limitations of machine-learning algorithmsProblem-solving aptitudeAnalytical mind and business acumenExcellent communication and presentation skills.
Skills and Experience:
Experience with B2B, Financial Industry, Asset Management, Sales & Marketing is highly desired.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.Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.Expertise querying Relational / No-SQL databases and using statistical programming languages like R, Python, etc.
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