https://bayt.page.link/fYRiEZg6iVkKFMmX8
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Job Description

SWATX is seeking a Data Scientist to analyze data and extract actionable insights that inform business decisions. This position entails working with extensive datasets, utilizing advanced analytical methods to create predictive models, and contributing to the development of the data science roadmap.


Key Responsibilities:


  1. Analyze telecom data to identify trends, patterns, and anomalies using data stores such as SQL Server and Cloudera over Hive.
  2. Develop and implement predictive models to improve customer experience and network performance.
  3. Design, build, and maintain scalable data science pipelines.
  4. Collaborate with cross-functional teams to understand business needs and deliver data-driven solutions.
  5. May participate in optimizing data pipelines, queries, and processes to improve performance and efficiency.
  6. Ensure data quality and integrity throughout the data lifecycle.
  7. Knowledge API exposure and consumption over Dataiku/Python is preferred if possible.
  8. Stay updated with the latest developments in data science technologies and best practices.

Qualifications:


  • Bachelor’s degree in Data Science, Statistics, Mathematics, or a related field.
  • 3+ years of experience in data analysis or data science in the telecommunications industry.
  • Knowledge of CRISP-DM methodology.

Skills:


  • Proficiency in SQL, Python is a must while cloudera and MLOps frameworks is a plus.
  • Strong statistical analysis and modeling skills.
  • Experience with big data technologies (Hadoop, Cloudera, Spark).
  • Knowledge in Geospatial and GIS data analysis and visualization and dealing with GIS files (KQL/KML).
  • Familiarity of REST API development which is preferred to be done on Dataiku
  • RAG/LLM knowledge
  • NLP and Classical Machine Learning Knowledge

Preferred Qualifications:


  • Proficiency or Certification in Dataiku.
  • Local GCC experience, preferably Saudi projects.
  • Experience in the telecommunications industry.
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