Data Scientist

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

We are seeking a motivated and detail-oriented Offshore Data Scientist to join a global, high-impact analytics initiative. This role is ideal for professionals who value technical excellence, continuous learning, and collaborative problem-solving in a structured, delivery-focused environment.

Key Responsibilities

• Understand business requirements thoroughly and translate them into effective, data-driven analytical solutions.
• Design, develop, and implement reliable analytics and anomaly detection solutions following established engineering and data science best practices.
• Build, maintain, and optimize data pipelines from multiple internal and external sources, including real-time sensor streams and environmental datasets.
• Develop, test, and validate algorithms for detecting abnormal patterns, performance degradation, data drift, and anomalies in time-series data.
• Participate in risk assessments, design discussions, and technical reviews to ensure solutions meet operational and business expectations.
• Deliver software in iterative development cycles with continuous testing, monitoring, and performance improvements.
• Support deployment of analytical models into production environments, ensuring stability, accuracy, and scalability.
• Work closely with cross-functional teams (engineering, operations, and business) and communicate analytical results clearly and professionally.
• Adhere to data governance, quality standards, and documentation practices throughout the development lifecycle.

Required Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Engineering, or a related technical discipline.
• 3+ years of hands-on experience in machine learning, time-series analytics, or applied data science roles.
• Strong programming skills in Python, with practical experience in data analysis, modeling, and automation.
• Solid understanding of statistical techniques, anomaly detection methods, drift analysis, and data quality monitoring.
• Experience working with sensor data or complex, heterogeneous data sources.
• Familiarity with software development best practices, version control systems (e.g., Git), and structured deployment workflows.
• Good working knowledge of SQL and relational databases.
• Strong analytical thinking, problem-solving ability, and attention to detail.
• A responsible, ownership-driven mindset with the ability to work independently and within a team.

Preferred Qualifications

• Master’s degree in Data Science, Statistics, or a related quantitative field.
• Experience with advanced machine learning techniques, ensemble models, and time-series forecasting.
• Practical exposure to data preprocessing, feature engineering, and integration of external or environmental datasets.
• Familiarity with cloud platforms and MLOps practices for model deployment and monitoring.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Exposure to industrial systems, operational analytics, or control systems is an advantage.

Important Note on the Interview Process

During interviews, the use of AI tools or external assistance is not permitted.

We place high value on honesty, integrity, and genuine technical understanding.

Candidates are expected to explain concepts and solutions in their own words. Reading responses verbatim from online sources will be evident and may lead to discontinuation of the interview process.

We highly appreciate candidates who demonstrate sincerity, preparation, and a willingness to learn.