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Data Scientist
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Based on Tennessee data
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What they do
A Data Scientist utilizes skills and experience to systematically answer questions using data to provide actionable recommendations. Commonly utilizes advanced statistical analysis and machine learning techniques. Common responsibilities also include data cleaning and data management.
$103,070 / year median in Tennessee
+25% projected growth
Job Description
Join us as a Data Scientist and support us in transforming raw data into actionable intelligence. You'll extract, clean, and analyze data to uncover trends and opportunities, ask insightful questions, and present your findings clearly to stakeholders. We're looking for someone with a strong background in statistics, proficiency in Python, R, SQL, and data visualization tools, and a creative problem-solver. Join us for an innovative environment, growth opportunities, and a collaborative team. Ready to make an impact? Apply today!
What We're Looking For:
Level of Formal Education:
A Bachelor's degree (BA, BS) or equivalent.
Area of Study:
Statistics, Applied Mathematics, Economics or related discipline. Master's degree preferred
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Years of Experience:
2 to 4 years' experience
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Type of Experience:
Experience in statistics.
You'll Go The Extra Mile If You Have:
Special Certifications or Technical Skills:
Job Interviewing/ Negotiations. Experience with Excel, PowerPoint, SQL, SAS, and experience with other programming languages (i.e.,R, Python,). Advanced pattern recognition and predictive modeling experience Solid statistical skill set is required like multiple linear regression, multivariate adaptive regression spline, cluster analysis, time series modeling, etc. A strong data skill set is also needed-- like SQL programming and SAS data step programming to get the data prepared for analysis and modeling.
Collaborate:
Work with senior data scientists and management to understand business needs.
Innovate:
Develop new statistical models for data analysis.
Communicate:
Share findings with stakeholders clearly.
Drive Insights:
Implement analytics for smarter business processes.
Stay Updated:
Keep up with industry developments.
Identify Opportunities:
Use data to solve business problems.
Data Mining:
Analyze data to improve products and strategies.
Evaluate Sources:
Assess new data sources and collection methods.
Custom Models:
Create tailored data models and algorithms.
Predictive Modeling:
Enhance customer experiences and business outcomes.
A/B Testing:
Manage the A/B testing framework.
Coordinate:
Work with teams to implement and monitor models.
Monitor Performance:
Develop tools to track model performance and data accuracy.