Technical > Data Scientist
Salary National Average
| Low | Medium | High |
|---|---|---|
| 93640.0000 | 116440.0000 | 149060.0000 |
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Candidate Supply: 14,093 Job Openings: 5,229
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Short Description:
A data scientist is a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. The data scientist role is an offshoot of several traditional technical roles, including mathematician, scientist, statistician and computer professional. This job requires the use of advanced analytics technologies, including machine learning and predictive modeling. A data scientist requires large amounts of data to develop hypotheses, make inferences, and analyze customer and market trends.
Duties / Responsibilities:
- Work with organizational stakeholders to identify opportunities for leveraging company data to drive business solutions.
- Mine and analyze data from company databases to optimize product development, marketing techniques, and business strategies.
- Assess the effectiveness and accuracy of new data sources and data-gathering techniques.
- Develop custom data models and algorithms to apply to data sets.
- Clean and validate data to ensure accuracy, completeness, and uniformity.
- Analyze data to identify patterns and trends.
- Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting, and other business outcomes.
- Develop company A/B testing framework and test model quality.
- Coordinate with different functional teams to implement models and monitor outcomes.
- Develop processes and tools to monitor and analyze model performance and data accuracy.
Skills / Requirements / Qualifications
- Education: Master's degree in Operations Research, Industrial Engineering, Applied Mathematics, Statistics, Physics, Computer Science, or related fields.
- Experience: 5-7 years of professional experience, including machine learning, AI, and being able to apply data science methods to real-world data problems.
- Computer skills: Proficient with programming languages (Java, C++, Python, R, SQL, Scala, etc.)
- Visualization tools: Experience utilizing visualization tools, such as Tableau, PowerBI, etc..
- Mathemeatical Skills: Strong mathematics skills (e.g., statistics, algebra)
- Misc. tools: Experience with big data technologies such as Hadoop and Spark and other common data science toolkits.
- Communication Skills: Ability to communicate complex data in a simple, actionable way. Ability to work independently and with team members from different backgrounds
- Miscellaneous Skills: Exceptional technical writing, analytical, and problem-solving skills. Excellent attention to detail.
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