Data Scientist - JP-555

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Data Scientist - JP-555

Dubai, United Arab Emirates (Full-time)

30th September 2024

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About The Job

Company Description

Creo Technologies is a global technology consulting firm based in Dubai, United Arab Emirates, specializing in delivering modern data-driven solutions to enterprises. The team at Creo consists of experienced technology professionals and domain experts offering scalable solutions in advanced analytics, machine learning, data visualization, and more. With a focus on various industries, Creo is committed to delivering excellence and helping clients unlock the full potential of their data.

Role Description

This is a full-time on-site role for a Data Scientist at Creo Technologies in Dubai, United Arab Emirates. The Data Scientist will be responsible for tasks such as data analytics, statistical modeling, data visualization, and data analysis on a day-to-day basis.

Technical Qualifications & Key Experience

• 5-7 years of experience in data-science.
• Data Engineering, Cleansing, Anomaly Detection and Modeling Skills
• Proficiency in Python, R, SQL, and ML frameworks like TensorFlow, Keras, Scikit-learn.
• Familiarity with DataOps and MLOps
• Experience in Alteryx, Databricks or SAS is a plus.
• Strong communication skills to articulate technical ideas to non-tech professionals.
• Ability to lead cross-functional teams in high-stakes environments.
• Problem-solving mindset with a focus on improving patient outcomes and operational efficiency.
• Lead projects from conception to deployment, ensuring on-time delivery and model success.
• Experience on complex models including LLM and on-premise Generative AI is a plus.
  
  

Educational Background

Bachelor's or Master’s degree in Statistics, Math, Computer Science, Data Science, or related fields.

Responsibilities

• Analyze large volumes of structured and unstructured data.
• Develop, validate, and deploy predictive and prescriptive models to improve analysis outcome.
• Implement models for natural language processing (NLP).
• Communicate technical findings to non-technical stakeholders, ensuring models' usability and addressing ethical or regulatory concerns (e.g., HIPAA).
• Collaborate with data engineers to ensure robust data pipelines for model training and deployment.
• Monitor and maintain models post-deployment, ensuring they are regularly updated with new data to improve accuracy and performance.