Data Mining Analyst — Actionable Insights & Visuals

Jing Hau; Akron (OH); On-site; USD 10,000 - 14,000; Full time

Benefits offered by this job

Free visa

Travel ticket

Job summary

Jing Hau in Akron, OH is seeking a Data Mining Analyst to join our team. You will gather, analyze, and interpret large datasets to identify patterns and trends that inform business decisions.

Responsibilities include collecting and cleaning data, applying statistical methods and machine learning, and presenting findings via clear visualizations and reports. You will work with cross-functional teams to turn data insights into improvements and monitor data quality.

Qualifications

• Bachelor's degree in Data Science, Statistics, or related field.
• Proven experience in data mining, analysis, and visualization.
• Strong knowledge of statistical methods and machine learning techniques.
• Proficient in SQL and programming languages such as Python or R.
• Excellent communication skills with the ability to present complex ideas in a simple manner.

Responsibilities

• Collect and clean data from various sources
• Use statistical methods and machine learning techniques to analyze data
• Identify correlations and patterns in large datasets
• Present findings in easily understandable visualizations and reports
• Collaborate with cross-functional teams to identify areas for improvement based on data insights
• Continuously monitor data quality and make recommendations for improvements

Skills

Data mining

Data analysis

Data visualization

Communication

Education

Bachelor's degree in Data Science, Statistics, or related field

Tools

SQL

Python

R

Job description

Jing Hau in Akron, OH is seeking a Data Mining Analyst to join our team. You will gather, analyze, and interpret large datasets to identify patterns and trends that inform business decisions.

Responsibilities include collecting and cleaning data, applying statistical methods and machine learning, and presenting findings via clear visualizations and reports. You will work with cross-functional teams to turn data insights into improvements and monitor data quality.