Lead Data Scientist

Staffwing Inc; San Jose (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Staffwing Inc is seeking a Lead Data Scientist in San Jose, California. This permanent position involves working with various data types to extract insights and drive business solutions.

The ideal candidate will have a strong background in mathematical statistics, machine learning, and data manipulation, with a demonstrated ability to communicate complex analyses effectively.

Qualifications

• Expertise in mathematics, applied statistics, and visualization.
• 5+ years of experience in business or consulting with leadership responsibilities.
• Proficiency in data analysis tools and statistical packages.

Responsibilities

• Work with structured and unstructured data to extract insights.
• Collaborate with cross-functional teams to solve business challenges.
• Develop predictive models using machine learning techniques.

Skills

Data analysis and manipulation

Statistical modeling

Machine learning techniques

Communication of quantitative analysis

Scripting language proficiency

Relational databases and SQL

Education

MS or Ph.D. in a quantitative field (e.g. Economics or Statistics)

Tools

R

Python

SQL

Hadoop

Job description

One of our direct client, a BigData & Data Science major is looking for a Lead Data Scientist to join their team of Data Scientists. This is a permanent position with client.

Job Description

Responsibilities:

Work on small and large data sets of structured, semi-structured, and unstructured data to discover hidden knowledge about the client’s business and develop methods to leverage that knowledge for their business.

Identify and solve business challenges working closely with cross-functional teams, such as Delivery, Business Consulting, Engineering and Product Management.

Develop prescriptive and predictive statistical, behavioral or other models via machine learning and/or traditional statistical modeling techniques, and understand which type of model applies best in a given business situation.

Drive the collection of new data and the refinement of existing data sources.

Analyze and interpret the results of product experiments.

Collaborate with the engineering and product teams to develop and support our internal data platform to support ongoing statistical analyses.

Qualifications

• A proven passion for generating insights from data.
• Comfort manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources.
• Expertise in mathematics and applied statistics, computer science, and visualization capabilities.
• Curious and an excellent learner. Able to research, explore and acquire working knowledge in new areas.
• A complete understanding of standard statistical techniques like MLE/QMLE, GMM, OLS/GLS, univariate and multivariate time series models (e.g. ARIMA, DLM's, VAR's), regression model diagnostics for time series and cross sectional data, along with Machine Learning methodologies like Random Forests, SVM's and Boosting/Bagging. The ideal candidate would know when and how to apply these alternative methodologies, and the relative advantages/disadvantages of each for a particular business case.
• Expert knowledge of an analysis tool/statistical package such as R, JMP, Stata, SPSS, SAS, Matlab
• Highly effective communicator; able to communicate complex quantitative analysis in a clear, precise & actionable way that is meaningful to general business audience and credible to client’s data scientists.
• Fluency with at least one scripting language such as Python, Java, or C/C++.
• Expertise with relational databases and SQL. NoSQL is a big plus.

REQUIREMENTS:

• MS or Ph.D. preferred in a quantitative Social Science (e.g. Economics) or Statistics with a substantive field interest.
• At least 5 years’ experience in business, consulting or applied field research with project lead responsibilities for solving analytics problems using quantitative approaches.
• Demonstrated track record producing models and actionable insights using advanced statistical methods.
• Experience working with large data sets using distributed computing tools, e.g. Map/Reduce, Hadoop, Hive, etc.

Additional Information

All your information will be kept confidential according to EEO guidelines.