Junior Data Scientist

Summary

Junior Data Scientist collects, cleans, and prepares geospatial data for mapping and AI-driven analysis using Python, SQL, and automation tools.

Category: Telecommunications

Location:

Data Extraction & Crawling:Assist in the automation of collecting geospatial data from APIs, databases, and web sources using tools like Selenium, Scrapy, or custom scripts.
Data Cleaning:Learn how to ensure the quality and consistency of geographic datasets by addressing missing or inconsistent data with guidance from senior engineers.
Data Preparation & Aggregation:Support in organizing and structuring geospatial data for use in xMap’s mapping and AI-driven analysis tools.
Data Manipulation:Work with geographic datasets to sort, filter, and transform them under the guidance of experienced team members.
Data Quality Assurance:Participate in implementing checks to maintain data integrity and geospatial accuracy.
Data Pipeline Development:Assist in building and maintaining automated data pipelines to support xMap’s platform.
Performance Optimization:Learn how to optimize data structures and queries for speed and efficiency in geospatial applications.
Automation & Process Improvement:Collaborate on automating repetitive tasks to streamline processes.
Collaboration & Communication:Work closely with xMap’s engineering team, GIS experts, and project managers, gaining experience in delivering high-quality data solutions.
Troubleshooting & Support:Assist in diagnosing and resolving issues in data pipelines, ensuring smooth operation of geospatial applications.

Knowledge of Data Manipulation Languages:Strong skills in Python and SQL, with a willingness to learn more about handling geospatial data.
Knowledge in Web Scraping:Exposure to tools like Selenium, BeautifulSoup, or Scrapy is a plus, but not required.
Geospatial Data Awareness:Eagerness to learn about geographic data formats such as GeoJSON and shapefiles.
Data Preparation Tools:Basic experience with pandas, NumPy, or other data manipulation libraries is helpful but can be developed on the job.
Data Quality Management:An interest in learning about data validation and ensuring accuracy in mapping datasets.
Pipeline Design:Willingness to develop skills in tools like Apache Airflow, Luigi, or similar, with a focus on data pipelines.
Problem-Solving Mindset:Eagerness to grow in troubleshooting skills for resolving issues in data pipelines.
Collaboration Skills:Ability to communicate effectively and work well with cross-functional teams.

Details