Data Scientist Asc

Description:Performs exploratory research and analysis to identify novel and meaningful patterns in data and uses statistical methods to reject or accept proposed hypotheses about relationships or latent predictive factors discovered through their work to provide business value. Uses a combination of tools, technologies, and numerical computing systems (i.e.: GPU processing, distributed computing, highly parallel coding, cloud computing, machine learning, visualization, system modelling and simulation) to achieve results. Areas of expertise should include several of the following: applied statistics, text mining, natural language processing, deep learning, optimization, and other similar fields.
Basic Qualifications:
- Work on datasets with applied statistics and machine learning algorithms
- Use exploratory data analysis techniques to identify meaningful relationships, patterns, or trends from complex data sets and discover opportunities in datasets to support decision-making
- Develop predictive models and new algorithms to solve data / business problems
- Understand the math and statistics behind the models and interpret, extrapolate, and prescribe from data to deliver actionable recommendations using effective visualizations
- Visualize and communicate findings and provide a quantitative framework for evaluating and analyzing alternatives
Desired Skills:
Foundational understanding of a broad set of tools to be able to apply the right tool to each data exploration activity. Tools could include, for example:

- Descriptive and Predictive modeling
- Machine Learning (supervised and unsupervised)
- Advanced statistics and statistical modeling
- Time series analysis and forecasting
- Optimization and simulation
- Communication, story-telling and visualization
- Distributed computing (Hadoop)
- Importing, cleaning, and transforming datasets
- Data warehousing
- Critical thinking & problem-solving skills
- Data preparation and transformation
- Languages: Python, Pig, Hive, Spark, SQL


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