The Data Scientist is responsible for analyzing large data sets to develop custom models and algorithms to drive business solutions. The Data Scientists work on project teams in order to provide analytical support to projects (for example, email targeting, business optimization, consumer recommendations) for Walmart eCommerce. They are responsible for building large data sets from multiple sources in order to build algorithms for predicting future data characteristics. Those algorithms will be tested, validated, and applied to large data sets. They are also responsible for training the algorithms so they can be applied to future data sets and provide the appropriate search results. Data Scientists are responsible for researching new trends in the industry and utilizing up-to-date technology (for example, HBase, MapReduce, LAPack, Gurobi) and analytical skills to support their assigned project.
Develop custom data models to drive innovative business solutions.
Work with cross-functional partners across the business.
Build complex data sets from multiple data sources, both internally and externally.
Conduct advanced statistical analysis to determine trends and significant data relationships.
Build learning systems to analyze and filter continuous data flows and offline data analysis.
Train algorithms to apply models to new data sets.
Validate models and algorithmic techniques.
Scale new algorithms to large data sets.
Combine data features to determine search models.
Research new techniques and best practices within the industry.
Develop models of current state in order to determine needed improvements
Utilize system tools including (Github, MySQL, Hadoop, Weka, R, Matlab,ILog)
- Understand different domains and identify problems that can leverage Machine Learning and Data Science in general.
- Extract, clean and scrub dataset from various sources like, distributed systems, databases etc.
Analysis of datasets and identifying problems to be solved. This requires a good understanding of domain, descriptive statistics and tests.
- Depending on the problem we build Time Series, Computer Vision, NLP or basic predictive models.
- 2+ years of relevant Data Science experience
- Bachelor of Science and 5 years' data science experience OR Master of
Science and 2 years' data science experience.
* ETS (Error Trend Seasonality) concepts
* Time Series Analysis (visualization, decomposition, data cleaning, time
feature engineering methods etc.)
* Expertise with traditional time series models like: Holt Winters Method,
ARMA, ARIMA, SARIMA.
* Experience with traditional predictive ML models
* Some Experience with complex models is favorable, like Prophet, STS,