Data Scientist

Emdad By Elm; Riyadh; On-site; SAR 100,000 - 150,000; Full time

Job summary

A data solutions company located in Riyadh is seeking a Data Scientist to analyze and model complex datasets. The role includes developing statistical models and machine learning algorithms while collaborating with cross-functional teams to drive data-driven decision-making. The ideal candidate should have a relevant degree and 2-5 years of experience in data science. Proficiency in Python and data analysis tools is essential for this position. This role is critical to enhancing organizational performance through actionable insights.

Qualifications

• Minimum of 2–5 years experience in data analysis or applied machine learning.
• Strong proficiency in programming languages such as Python or R.
• Solid understanding of statistical analysis and hypothesis testing.
• Experience with relational and non-relational databases.

Responsibilities

• Collect, clean, and preprocess structured and unstructured data.
• Analyze large datasets to identify trends and insights.
• Design and validate statistical models and machine learning algorithms.
• Collaborate with teams to translate business requirements into analytical solutions.
• Develop visualizations and reports to communicate findings.

Skills

Data Analysis and Statistical Modeling

Machine Learning and Predictive Analytics

Problem-Solving and Critical Thinking

Data Visualization and Storytelling

Stakeholder Collaboration

Attention to Detail and Data Quality

Time Management and Prioritization

Ethical Data Usage and Privacy Awareness

Education

Bachelor’s degree in Data Science or related field

Master’s degree in Data Science or related discipline

Tools

Python

R

Pandas

NumPy

SciPy

Scikit-learn

TensorFlow

PyTorch

SQL

Job description

The Data Scientist is responsible for analyzing, modeling, and interpreting complex datasets to generate actionable insights and support data-driven decision‐making. The role focuses on developing statistical models, machine learning algorithms, and analytical solutions that enhance organizational performance, optimize products and services, and enable strategic planning.

Key Responsibilities:

• Collect, clean, and preprocess structured and unstructured data from multiple sources.
• Analyze large datasets to identify trends, patterns, and insights that support business and strategic objectives.
• Design, develop, and validate statistical models and machine learning algorithms.
• Build predictive and prescriptive analytics solutions to support decision‑making.
• Collaborate with cross‑functional teams to translate business requirements into analytical solutions.
• Develop data visualizations, dashboards, and reports to communicate findings to technical and non‑technical stakeholders.
• Evaluate model performance and continuously improve accuracy, robustness, and scalability.
• Ensure data quality, integrity, and consistency across analytical outputs.
• Apply best practices in data governance, privacy, and ethical use of data.
• Document methodologies, models, and analytical processes.

Job Requirements:

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Master’s degree in Data Science, Artificial Intelligence, Statistics, or a related discipline is preferred.
• Minimum of 2–5 years of experience in data analysis, data science, or applied machine learning.
• Strong proficiency in programming languages such as Python or R.
• Experience with data manipulation and analysis libraries (e.g., Pandas, NumPy, SciPy).
• Hands‑on experience with machine learning frameworks (e.g., Scikit‑learn, TensorFlow, PyTorch).
• Solid understanding of statistical analysis, probability, and hypothesis testing.
• Experience with SQL and working with relational and non‑relational databases.
• Ability to communicate complex analytical concepts clearly to non‑technical audiences.

Required Skills and Competencies:

• Data Analysis and Statistical Modeling
• Machine Learning and Predictive Analytics
• Problem‑Solving and Critical Thinking
• Data Visualization and Storytelling
• Stakeholder Collaboration
• Attention to Detail and Data Quality
• Time Management and Prioritization
• Ethical Data Usage and Privacy Awareness

Expected Outcomes:

• Actionable insights that support strategic and operational decision‑making.
• Reliable and scalable data models that improve efficiency and performance.
• Improved data‑driven culture across the organization.
• Measurable impact through analytics and predictive insights.