Career opportunities-Data Scientist (Permanent positions)# DataSciences - 数据科学
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We have a few immediate permanent Data Scientist positions.If you are
qualified and available, please send an e-mail to [email protected]
/* */ with
an update-to-date word resume.
有意者，请火速联系。
倾尽全力推荐同胞!!!
Thanks
1. Lead Data Scientist
Location: Bay Area
Responsibilities:
Design best structure and select the most appropriate modeling
techniques which include a variety of machine learning algorithms.
Analyze data covering a wide range of information from user profile to
transaction history.
Identify new and emerging patterns through data mining.
Build and validate financial forecasting models, provide analysis
support, and develop new statistical forecasting criteria and/or techniques
Lead and manage the design and optimization of predictive algorithms and
/ or processes using advanced statistical / mathematical forecasting
approaches.
Iteratively test, refine and improve the models.
On a regular basis, ensure models and methodologies are statistically
valid and provide fact based interpretation of performance.
Research, analyze and triangulate information on various loss trends to
determine significance, assess implications and provide analysis with
recommended actions
Work with large volumes of data; extract and manipulate large datasets
using tools such as SQL, SAS, and Hadoop
Closely work with Finance, Risk BU, IT, Engineering and other data/
system providers to enhance systems, trouble-shoot data issues, etc.
Qualification:
Master’s in Mathematics, Statistics, Financial Engineering or
equivalent is desired. MBA is preferred
8-10 years of post-college working experience within a Quantitative
Research, Statistical Modeling or Quant FP&A organization
Creative problem solver, analytic thinker, and quick learner. Strong
conceptual and creative problem-solving skills; ability to work with
considerable ambiguity; ability to learn new and complex concepts quickly.
Relentlessly resourceful and scrappy
Required technical skills: MS Excel (with VBA), Teradata SQL and SAS.
Working knowledge of Map-Reduce, Hive, R, Pig
Hands-on experience with SAP Bank Analyzer and Tableau is preferred
A great communicator, strong project management skills, and superb
attention to details
2.Lead Data Scientist for Mobile Gaming company
Location: Los Angeles/New York
Responsibilities:
Analysis, design, and test of complex data systems involving: predictive
score models, feature extraction, clustering & segmentation, application of
machine learning algorithms, and monitoring of predictive tool performance
Design new algorithmic approaches to solving difficult quantitative problems
Mine game data and user trends to evaluate and quantify the effects of
ongoing changes to games through statistical models
Predict Life Time Value (LTV) of customers
Build in-game pricing models to optimize in-game economies
Design A/B and multi-variant testing methodology
Qualification:
MS or PhD in Computer Science, Statistics, Math, Engineering, or another
quantitative discipline
Prior statistical score modeling and/or machine learning experience
including delivery of models and algorithms
Expert use of statistical tools, such as R or Python; proficiency in C++
, Java, etc.
Experience with Spark, Hadoop, Hive, and Pig
Ability to implement algorithms (code) and produce working API's that
can drive recommendation engines.
Experience and proficiency in emerging big data analytics tools and
architecture.
Experience with A/B testing.
qualified and available, please send an e-mail to [email protected]
/* */ with
an update-to-date word resume.
有意者，请火速联系。
倾尽全力推荐同胞!!!
Thanks
1. Lead Data Scientist
Location: Bay Area
Responsibilities:
Design best structure and select the most appropriate modeling
techniques which include a variety of machine learning algorithms.
Analyze data covering a wide range of information from user profile to
transaction history.
Identify new and emerging patterns through data mining.
Build and validate financial forecasting models, provide analysis
support, and develop new statistical forecasting criteria and/or techniques
Lead and manage the design and optimization of predictive algorithms and
/ or processes using advanced statistical / mathematical forecasting
approaches.
Iteratively test, refine and improve the models.
On a regular basis, ensure models and methodologies are statistically
valid and provide fact based interpretation of performance.
Research, analyze and triangulate information on various loss trends to
determine significance, assess implications and provide analysis with
recommended actions
Work with large volumes of data; extract and manipulate large datasets
using tools such as SQL, SAS, and Hadoop
Closely work with Finance, Risk BU, IT, Engineering and other data/
system providers to enhance systems, trouble-shoot data issues, etc.
Qualification:
Master’s in Mathematics, Statistics, Financial Engineering or
equivalent is desired. MBA is preferred
8-10 years of post-college working experience within a Quantitative
Research, Statistical Modeling or Quant FP&A organization
Creative problem solver, analytic thinker, and quick learner. Strong
conceptual and creative problem-solving skills; ability to work with
considerable ambiguity; ability to learn new and complex concepts quickly.
Relentlessly resourceful and scrappy
Required technical skills: MS Excel (with VBA), Teradata SQL and SAS.
Working knowledge of Map-Reduce, Hive, R, Pig
Hands-on experience with SAP Bank Analyzer and Tableau is preferred
A great communicator, strong project management skills, and superb
attention to details
2.Lead Data Scientist for Mobile Gaming company
Location: Los Angeles/New York
Responsibilities:
Analysis, design, and test of complex data systems involving: predictive
score models, feature extraction, clustering & segmentation, application of
machine learning algorithms, and monitoring of predictive tool performance
Design new algorithmic approaches to solving difficult quantitative problems
Mine game data and user trends to evaluate and quantify the effects of
ongoing changes to games through statistical models
Predict Life Time Value (LTV) of customers
Build in-game pricing models to optimize in-game economies
Design A/B and multi-variant testing methodology
Qualification:
MS or PhD in Computer Science, Statistics, Math, Engineering, or another
quantitative discipline
Prior statistical score modeling and/or machine learning experience
including delivery of models and algorithms
Expert use of statistical tools, such as R or Python; proficiency in C++
, Java, etc.
Experience with Spark, Hadoop, Hive, and Pig
Ability to implement algorithms (code) and produce working API's that
can drive recommendation engines.
Experience and proficiency in emerging big data analytics tools and
architecture.
Experience with A/B testing.