Junior Data Science Analyst

Role Overview

As a Junior Data Science Analyst, you will assist in analyzing data, creating visualizations, and generating insights to support decision-making processes. You will work closely with senior data scientists and other team members to develop data-driven solutions and enhance our analytical capabilities.

Key Responsibilities

• Data Analysis: Collect, clean, and analyze data from various sources. Utilize statistical techniques and tools to identify trends, patterns, and anomalies.
• Reporting & Visualization: Create dashboards, reports, and visualizations to communicate findings clearly and effectively to stakeholders.
• Model Development: Assist in developing, testing, and implementing predictive models and algorithms under the guidance of senior team members.
• Collaboration: Work with cross-functional teams to understand business needs and provide data-driven recommendations.
• Documentation: Maintain thorough documentation of data processes, methodologies, and analyses to ensure transparency and reproducibility.
• Continuous Learning: Stay updated with industry trends, tools, and techniques to continuously improve your skills and knowledge.
  
  

Qualifications

• Education: Bachelor’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
• Experience: 1-2 years of experience in data analysis or a related role, including internships or academic projects.
• Technical Skills: Proficiency in programming languages such as Python or R. Experience with data manipulation libraries (e.g., pandas, numpy) and data visualization tools (e.g., Tableau, Matplotlib).
• Analytical Skills: Strong quantitative and analytical skills with a keen eye for detail.
• Communication Skills: Ability to communicate complex data insights in a clear and concise manner.
• Team Player: Proven ability to work collaboratively in a team environment and manage multiple tasks effectively.
  
  

Preferred Skills

• Familiarity with SQL for database querying.
• Experience with machine learning frameworks and libraries.
• Knowledge of big data technologies such as Hadoop or Spark.
  
  

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