Offshore Data Scientist: Time-Series & Anomaly Analytics

Hermes Corporate; Makkah Region; On-site; SAR 224,971 - 299,962; Full time

Job summary

A leading global analytics firm is seeking an Offshore Data Scientist to deliver data-driven analytical solutions in a structured environment. The role involves designing and implementing effective analytics, building data pipelines, and collaborating closely with cross-functional teams. Ideal candidates will have a bachelor's degree in a relevant field and at least 3 years of experience in machine learning or applied data science. The position offers an opportunity for technical excellence and continuous learning in Makkah Region, Saudi Arabia.

Qualifications

• 3+ years of hands-on experience in machine learning or applied data science roles.
• Solid understanding of statistical techniques and anomaly detection methods.
• Experience working with sensor data or complex, heterogeneous data sources.

Responsibilities

• Understand business requirements and translate them into analytical solutions.
• Design and implement reliable analytics and anomaly detection solutions.
• Build and optimize data pipelines from multiple internal and external sources.

Skills

Machine Learning

Data Analysis

Python

Statistical Techniques

Anomaly Detection

SQL

Data Governance

Problem Solving

Education

Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, or related field

Tools

TensorFlow

PyTorch

Git

Job description

A leading global analytics firm is seeking an Offshore Data Scientist to deliver data-driven analytical solutions in a structured environment. The role involves designing and implementing effective analytics, building data pipelines, and collaborating closely with cross-functional teams. Ideal candidates will have a bachelor's degree in a relevant field and at least 3 years of experience in machine learning or applied data science. The position offers an opportunity for technical excellence and continuous learning in Makkah Region, Saudi Arabia.