Senior Data Scientist

4 days ago

Riyadh, Ar Riyāḑ, Saudi Arabia Quant Data & Analytics Full time

Quant Data & Analytics Company | Full time

Senior Data Scientist

Riyadh, Saudi Arabia | Posted on 11/06/2024

Quant, a pioneering Saudi enterprise, leads the charge in digital transformation by leveraging the vast array of data resources available in the fourth industrial revolution. We are deeply committed to delivering exceptional Data Science and AI SaaS products and solutions, with a specialized focus on the real estate sector through Suhail and the retail sector through Fruits360. Our talented dream team possesses unique expertise, enabling us to achieve a track record of marvels, from analyzing more than a trillion data records to serving hundreds of clients across more than 10 industries. Our strategic international and local partnerships alongside our expertise have positioned us as trailblazers in the global transition towards data-driven economies.

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:

  1. Collaborate with executive stakeholders to understand strategic needs and challenges through data, devising sophisticated solutions to drive business impact.
  2. Lead the mining and analysis of data from various databases, ensuring alignment with organizational objectives to derive actionable optimization recommendations.
  3. Research, develop, and implement advanced statistical learning models for in-depth data analysis and predictive insights.
  4. Introduce and integrate cutting-edge statistical and mathematical methodologies as required for specific projects and analysis.
  5. Optimize collaborative development efforts through strategic database utilization and robust project design.
  6. Enhance data collection procedures to ensure the inclusion of comprehensive information critical for advanced analytic systems.
  7. Develop and maintain automated anomaly detection systems, ensuring rigorous performance monitoring and improvement.
  8. Writing comprehensive reports and crafting high-level presentations that clearly communicate project deliverables, findings, and strategic recommendations to key decision makers.
  9. Integrate external third-party data sources to enrich company data and augment analytical capabilities.
  10. Stay abreast of emerging technologies and industry trends, driving continuous innovation within the data science team.
Requirements

Skills Required:

  1. A minimum of four years of comprehensive experience in data science, including demonstrated success in leading projects.
  2. Strong proficiency in Python programming and data science frameworks such as scikit-learn, pandas, numpy, scipy etc.
  3. Work experience in deep learning frameworks such as Pytorch, Tensorflow, Keras, etc.
  4. Has a strong understanding of the fundamentals of machine learning, probability and statistics and algorithms.
  5. Proficiency in SQL and experience with relational databases.
  6. Experience in data visualization tools such as Matplotlib, Seaborn, Tableau and/or Power BI.
  7. Excellent problem-solving and analytical abilities and attention to details.
  8. Good grasp of data engineering and devOps concepts and tools and the best practices of software development.

Skills Preferred:

  1. Experience in working in geospatial data analytics and time series forecasting.
  2. Familiarity with big data frameworks like Hadoop or Spark.
  3. Experience in computer vision libraries and frameworks such as OpenCV, scikit-image, or SimpleCV.
  4. Familiarity with advanced computer vision techniques such as object detection, image segmentation, and facial recognition.
  5. Understanding of NLP, graph neural networks and reinforcement learning fundamentals.
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