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Permanent Computer Science Statistics Jobs in Georgia

Bachelor's degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering). Master's or Ph.D. is a plus. * Experience: 5 to 10 years in data science, with experience in ...

Master of Science degree or higher in the fields of Computer Science, Statistics, or Mathematics is preferred * An aptitudefor medical informatics is preferred WHAT YOU'LL NEED Additional

Master of Science degree or higher in the fields of Computer Science, Statistics, or Mathematics is preferred * An aptitudefor medical informatics is preferred WHAT YOU'LL NEED Additional

MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.) or equivalent work experience. * 1+ years of experience in modern AI/ML tools and ...

MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.) or equivalent work experience. * 1+ years of experience in modern AI/ML tools and ...

Bachelors degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering); a Masters or Ph.D. is a plus. * 5 to 10 years of experience in data science, including machine ...

MS, or PhD in an appropriate technology field (Computer Science, Statistics, Applied Math, Operations Research, etc.) or equivalent work experience. * 1+ years of experience in modern AI/ML tools and ...

Bachelor's degree in Computer Science, Statistics, Data Science, Machine Learning, or related quantitative discipline, or equivalent professional experience. Master's or PhD preferred. * 3+ years of ...

Education Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field, or combination of education and equivalent experience. Location:

Education Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field, or combination of education and equivalent experience. Location:

Bachelor's degree in a relevant field (e.g., Data Science, Computer Science, Statistics) * Proven experience in data analysis and visualization, with a strong focus on Power BI * Strong knowledge of ...

D. in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, Operations Research, or related disciplines. * 18-20 years of total experience, with 13-15 years in Data Science/ML ...

Bachelor's degree in a relevant field (e.g., Data Science, Computer Science, Statistics) * Proven experience in data analysis and visualization, with a strong focus on Power BI * Strong knowledge of ...

Bachelor's degree in Computer Science, Statistics, Data Science, Engineering, Operations Research, or a related technical field; or equivalent professional experience * 3+ years hands-on experience ...

D. degree in Data Science, Computer Science, Statistics, Mathematics, or related quantitative field, or combination of Bachelor's degree with equivalent experience. Location : Duluth, GA #HP1 Equal ...

Bachelor's or Master's degree in data science, statistics, mathematics, computer science, engineering, or related field * 2+ years of relevant experience in data science, analytics, or a related role ...

Bachelor's or Master's degree in data science, statistics, mathematics, computer science, engineering, or related field * 2+ years of relevant experience in data science, analytics, or a related role ...

Showing results 21-40

Permanent Computer Science Statistics information

What is the difference between Permanent Computer Science Statistics vs Permanent Data Analyst?

AspectPermanent Computer Science StatisticsPermanent Data Analyst
Required CredentialsBachelor's or higher in Computer Science, Statistics, or related fields; often certifications in data analysis or programmingBachelor's in Statistics, Data Science, or related fields; certifications in data tools are common
Work EnvironmentTech companies, research institutions, or finance; focus on algorithms and statistical modelsBusiness, marketing, finance; focus on data interpretation and reporting
Employer & Industry UsageUsed in tech, finance, academia for developing models and algorithmsUsed across industries for analyzing data to inform business decisions

Permanent Computer Science Statistics professionals focus on developing algorithms and statistical models, often in tech or research settings, while Permanent Data Analysts interpret data to support business strategies across various industries. Both roles require strong analytical skills and relevant certifications, but their daily tasks and work environments differ.

Is statistics useful in computer science?

Statistics is a fundamental skill for computer science professionals, including those in data analysis, machine learning, and algorithm development. It helps in understanding data patterns, making informed decisions, and developing models, often using tools like R or Python. Proficiency in statistics enhances problem-solving and analytical capabilities in the field.
What cities in Georgia are hiring for Permanent Computer Science Statistics jobs? Cities in Georgia with the most Permanent Computer Science Statistics job openings:

Data Scientist 2 4P/187

4P Consulting Inc.

Atlanta, GA • On-site

Contractor

Re-posted 5 days ago


Job description

Data Scientist (5–10 Years Experience)
Overview:

A Data Scientist with 5 to 10 years of experience is responsible for leveraging data to uncover insights, create predictive models, and drive data-driven decision-making within an organization. This role requires advanced analytics, machine learning expertise, and strong problem-solving skills to extract actionable intelligence from large and complex datasets.


Key Responsibilities:

1. Data Analysis:

  • Collect, clean, and analyze complex datasets to uncover trends, patterns, and actionable insights.

  • Apply statistical techniques to derive meaningful information for business strategies.

2. Predictive Modeling:

  • Develop and deploy machine learning models to forecast future trends, behaviors, and outcomes.

  • Utilize techniques such as regression analysis, classification, and clustering.

3. Data Visualization:

  • Create compelling visualizations using tools like Tableau, Power BI, and Python libraries (e.g., Matplotlib, Seaborn).

  • Effectively communicate insights to both technical and non-technical stakeholders.

4. Hypothesis Testing:

  • Formulate and test hypotheses to statistically validate business decisions and recommendations.

5. Feature Engineering:

  • Engineer and select relevant features to optimize the performance of machine learning models.

6. Algorithm Development:

  • Build and fine-tune machine learning algorithms such as decision trees, random forests, and neural networks.

7. Data Integration:

  • Collaborate with IT and database administrators to access and integrate data from multiple sources and data warehouses.

8. Model Deployment:

  • Deploy machine learning models into production environments to support real-time analytics and decision-making.

9. A/B Testing:

  • Design and evaluate A/B tests to assess the impact of process or product changes.

10. Data Ethics:

  • Ensure data handling practices meet ethical standards, including privacy and compliance with regulations.

11. Cross-functional Collaboration:

  • Work closely with engineers, business analysts, and domain experts to align data initiatives with business goals.

12. Mentorship:

  • Provide guidance and mentorship to junior data scientists and analysts to support team development.

13. Continuous Learning:

  • Stay updated on the latest data science tools, trends, and best practices through professional development.


Qualifications:
  • Education: Bachelor’s degree in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering).
    Master’s or Ph.D. is a plus.

  • Experience: 5 to 10 years in data science, with experience in machine learning and statistical analysis.

  • Programming Languages & Tools: Proficiency in Python, R, or Julia.

  • Visualization Tools: Experience with Tableau, Power BI, and Python visualization libraries (Matplotlib, Seaborn).

  • Database Skills: Strong understanding of databases and SQL-based data manipulation.

  • Additional Skills:

    • Advanced problem-solving and critical thinking abilities.

    • Strong communication skills for conveying technical findings to diverse audiences.

    • Familiarity with big data and distributed computing frameworks (e.g., Hadoop, Spark) is a plus.

    • Awareness of data ethics and regulatory compliance.