Senior Data Scientist

QUANT; Riyadh; On-site; SAR 120,000 - 180,000; Full time

Job summary

A leading company specializing in Data Science is seeking a Senior Data Scientist to tackle complex business challenges. The candidate will analyze vast datasets to derive actionable insights, develop advanced predictive models, and communicate findings effectively to stakeholders. A strong technical background in Python, machine learning, and data visualization is essential for success in this role.

Qualifications

• Minimum four years of experience in data science.
• Strong proficiency in Python and data science frameworks.
• Experience with deep learning and machine learning fundamentals.

Responsibilities

• Collaborate with stakeholders to devise data-driven solutions.
• Lead data mining and analysis for optimization recommendations.
• Develop and implement statistical learning models.

Skills

Python programming

Data science frameworks

Deep learning frameworks

Machine learning fundamentals

SQL proficiency

Data visualization tools

Problem-solving abilities

Data engineering concepts

Tools

scikit-learn

pandas

numpy

scipy

Pytorch

Tensorflow

Keras

Matplotlib

Seaborn

Tableau

Power BI

Job description

The core of Quant is Data Science, and hence it requires highly capable individuals that can deliver the level of quality our clients have grown accustomed to, and to have a mindset that’s highly agile, critical and curious. The Senior Data Scientist is required to collect, analyze and interpret large data sets in order to develop data-driven solutions, insights, and visualizations for difficult business challenges.

Job Responsibilities:

• Collaborate with executive stakeholders to understand strategic needs and challenges through data, devising sophisticated solutions to drive business impact.
  

  
  

• Lead the mining and analysis of data from various databases, ensuring alignment with organizational objectives to derive actionable optimization recommendations.
  

  
  

• Research, develop, and implement advanced statistical learning models for in-depth data analysis and predictive insights.
  

  
  

• Introduce and integrate cutting-edge statistical and mathematical methodologies as required for specific projects and analysis.
  

  
  

• Optimize collaborative development efforts through strategic database utilization and robust project design.
  

  
  

• Enhance data collection procedures to ensure the inclusion of comprehensive information critical for advanced analytic systems.
  

  
  

• Develop and maintain automated anomaly detection systems, ensuring rigorous performance monitoring and improvement.
  

  
  

• Writing comprehensive reports and crafting high-level presentations that clearly communicate project deliverables, findings, and strategic recommendations to key decision makers.
  

  
  

• Integrate external third-party data sources to enrich company data and augment analytical capabilities.
  

  
  

• Stay abreast of emerging technologies and industry trends, driving continuous innovation within the data science team.
  

  
  

Requirements

Skills Required:

• A minimum of four years of comprehensive experience in data science, including demonstrated success in leading projects.
  

  
  

• Strong proficiency in Python programming and data science frameworks such as scikit-learn, pandas, numpy, scipy etc.
  

  
  

• Work experience in deep learning frameworks such as Pytorch, Tensorflow, Keras, etc.
  

  
  

• Has a strong understanding of the fundamentals of machine learning, probability and statistics and algorithms.
  

  
  

• Proficiency in SQL and experience with relational databases.
  

  
  

• Experience in data visualization tools such Matplotlib, Seaborn, Tableau and/or Power BI.
  

  
  

• Excellent problem-solving and analytical abilities and attention to details.
  

  
  

• Good grasp of data engineering and devOps concepts and tools and the best practices of software development.
  

  
  

Skills Preferred:

• Experience in working in geospatial data analytics and time series forecasting.
  

  
  

• Familiarity with big data frameworks like Hadoop or Spark.
  

  
  

• Experience in computer vision libraries and frameworks such as OpenCV, scikit-image, or SimpleCV.
  

  
  

• Familiarity with advanced computer vision techniques such as object detection, image segmentation, and facial recognition.
  

  
  

• Understanding of NLP, graph neural networks and reinforcement learning fundamentals.