Ahmedabad, Gujarat, India
Information Technology
Full-Time
Wolters Kluwer
Overview
About the Role:
As a Data Scientist, you'll play a crucial role in supporting data-driven projects, performing routine data analysis tasks, and assisting with model development and testing. Your role involves contributing to various stages of the data science lifecycle and providing insights that help shape business strategies. This position is suited for those with a foundational understanding of data science and are eager to deepen their expertise.
Responsibilities:
As a Data Scientist, you'll play a crucial role in supporting data-driven projects, performing routine data analysis tasks, and assisting with model development and testing. Your role involves contributing to various stages of the data science lifecycle and providing insights that help shape business strategies. This position is suited for those with a foundational understanding of data science and are eager to deepen their expertise.
Responsibilities:
- Contribute to more advanced data collection and preprocessing efforts.
- Assist in developing and testing machine learning models.
- Perform descriptive data analysis and provide preliminary insights.
- Create visualizations and reports to support data findings.
- Manage and maintain data repositories efficiently.
- Support senior data scientists in feature creation and selection.
- Participate in collaborative projects with cross-functional teams.
- Help automate data processing tasks using scripts.
- Contribute to model validation and performance testing.
- Stay informed about emerging data science tools and methodologies.
- Advanced Data Cleaning: Enhanced preprocessing and data wrangling techniques.
- Intermediate Python: Proficiency in Python for data science tasks.
- SQL: More advanced SQL querying skills.
- Data Visualization: Ability to create meaningful visualizations using tools like Tableau or Matplotlib.
- Machine Learning Basics: Understanding of basic machine learning concepts and algorithms.
- Statistical Analysis: Intermediate statistical methods and their applications.
- Communication: Articulating data insights clearly to non-technical stakeholders.
- Problem-Solving: Effective in identifying data-related issues and providing solutions.
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