About the Role
Baynovation is hiring a Data Analyst Intern to join our team in San Jose, CA.
Come join our collaborative and work closely with a team of data analysts and professionals to gather, organize, analyze, and present data in a meaningful way. We are waiting for you to join us and do the best work of your life.
Requirements
Education: Currently pursuing a degree in a relevant field such as data science, statistics, mathematics, computer science, or a related discipline. Some companies may also consider candidates with a high school diploma or equivalent if they have relevant experience or coursework.
Analytical skills: Strong analytical thinking and problem-solving abilities to process complex data sets, identify patterns, and derive meaningful insights. Familiarity with statistical concepts and techniques is desirable.
Technical skills: Proficiency in data manipulation and analysis using tools such as Microsoft Excel, SQL, Python, R, or other statistical software. Knowledge of data visualization tools like Tableau, Power BI, or D3.js is a plus.
Teamwork and collaboration: Ability to work effectively in a team environment, collaborate with colleagues, and contribute to shared goals and projects.
Time management: Strong organizational skills and the ability to manage multiple tasks and meet deadlines in a fast-paced work environment.
Responsibility
Data collection and preparation: Assisting in the collection and organization of data from various sources, including databases, spreadsheets, and external systems. This may involve cleaning and formatting data to ensure accuracy and consistency.
Data analysis: Conducting data analysis using statistical techniques and data visualization tools to identify trends, patterns, and insights. This may involve creating reports, charts, and graphs to present findings to the team or management.
Data modeling: Assisting in the development and implementation of data models or algorithms to support business objectives or solve specific problems. This may include using statistical software, programming languages, or machine learning tools.
Data validation and quality assurance: Reviewing and validating data to ensure its accuracy, completeness, and consistency. Identifying and addressing any data quality issues or anomalies that may arise during the analysis process.
Continuous learning: Staying up-to-date with the latest data analysis techniques, tools, and industry trends. Actively seeking opportunities to expand knowledge and skills in areas such as data visualization, statistical analysis, and data manipulation.