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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.