Data Scientist

About Presight

Presight, an ADX-listed public company limited by shares whose majority shareholder is Abu Dhabi company G42, is the region’s leading big data analytics company powered by Artificial Intelligence (“AI”). It combines big data, analytics, and AI expertise to serve every sector, of every scale, to create business and positive societal impact. With its world-class computer vision, AI and omni-analytics platform as its engine, Presight leverages all-source data to support insight-driven decision making that shapes policy and creates safer, healthier, happier, and more sustainable societies.

The Opportunity:

Are you a qualified and proficient Data Scientist with the proven ability to drive business growth & operational efficiency? Presight needs your skills to develop and deliver diverse data products across the company—including, but not limited to, recommendation engines, predictive models, business-logic automation and machine learning.

What working at Presight offers:

Culture: An open, diverse and inclusive environment with a global vision that encourages personal growth and focuses on ground-breaking, industry-first innovations.

Career:

Rewards: A competitive remuneration package with a host of perks including healthcare, education support, leave benefits and more.

Key Responsibilities:

• Problem Formulation & Data Exploration:
• Collaborate with stakeholders to understand business needs and translate them into well-defined data science problems.
• Identify relevant data sources and assess their quality and suitability for analysis.
• Perform exploratory data analysis (EDA) to uncover patterns, trends, and anomalies.
• Data Preparation & Feature Engineering:

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• Clean, transform, and process data to ensure its readiness for modelling.
• Design and implement feature engineering techniques to extract meaningful information from raw data.
• Develop and maintain data pipelines for efficient data ingestion and processing.
• Machine Learning Model Development & Deployment:
• Select and apply appropriate machine learning algorithms (e.g., regression, classification, clustering) to build predictive and prescriptive models.
• Experiment with and evaluate different models to select the best approach for each use case.
• Optimize models for performance, accuracy, and scalability.
• Deploy models to production environments (e.g., Azure ML/ Containerized ML Model with REST End pint) and monitor their performance.
• Familiarity with LLM application libraries and frameworks (e.g., LangChain, LlamaIndex, Dify).
• Experience working with deep learning models and libraries, as well as GPU acceleration libraries (e.g., CUDA, cuDNN) for optimizing model training and inference.