Healthcare Data Scientist

**HIRING**

Reporting to: Director of AI Science

Job Title - Healthcare Data Scientist

Salary- 30,000 - 40,000 AED Per Month

Perm Role - Full Time

Location - Dubai

• My client will provide a flight for Individual if relocating to the UAE - This does NOT include family
• Flights home once a year to the individual - This does NOT include family
• Bonus - Not Guaranteed - Based on company and personal performance - Bonus will not be on offer letter
• The client will provide Medical Insurance for induvial and family
• Visa - Only for the individual - This does NOT include the family
• One Month into single accommodation - Studio / Hotel Room / 1 Bed

About

My client is a leading integrated healthcare company with operations across the UAE, UK, Europe and the US.

About the Role:

As a Data Scientist in the AI team, you will be a critical technical expert responsible for the rigorous assessment and validation of third-party AI solutions. Working closely with Product Managers, AI Architects, and domain experts, you will dive deep into vendor offerings, design innovative evaluation methodologies, and conduct hands-on testing to verify claims, assess performance, and determine the suitability of these solutions for the clients' diverse needs (Provider, Payer, Enterprise, Consumer).

Your role is not primarily to build AI models from scratch, but to be a discerning and creative evaluator. You will leverage your deep understanding of AI/ML principles, data analysis techniques, and validation frameworks to ensure that the customer invests in robust, effective, and ethically sound AI technologies.

Key Responsibilities:

• Conduct in-depth technical reviews of AI vendor solutions, including their underlying methodologies, algorithms, data requirements, model architecture (where disclosed), and performance claims.
• Analyse vendor documentation, APIs, and technical specifications to understand solution capabilities and limitations
• Design and implement creative and robust evaluation frameworks and testing protocols for diverse AI solutions (e.g., predictive models, NLP tools, computer vision algorithms).
• Define appropriate performance metrics, statistical significance tests, and bias detection techniques relevant to specific use cases and solution types.
• Execute evaluation plans, including data preparation, feature engineering (for comparative analysis if needed), running vendor models/APIs, and analyzing outputs.
• Interpret complex evaluation results and translate them into clear, actionable insights for both technical and non-technical stakeholders
• Collaborate closely with Product Managers to align technical validation with business requirements and POC objectives.
• Work with AI Architects and technical teams to understand integration feasibility and data pipeline requirements for evaluated solutions.

Qualifications & Experience:

• Master’s degree or PhD in Data Science, Computer Science, Statistics, Mathematics, Physics, or a related quantitative field
• 3-5+ years of hands-on experience as a Data Scientist or Machine Learning Engineer, with a strong portfolio of projects demonstrating expertise in AI/ML model development, evaluation, and deployment.
• Proficiency in programming languages commonly used in data science (e.g., Python, R) and relevant libraries (e.g., scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
• Solid understanding of a wide range of machine learning algorithms (classification, regression, clustering, NLP, computer vision) and their underlying mathematical principles.
• Demonstrable experience in designing and executing model validation strategies, including cross-validation, A/B testing, and bias assessment.
• Strong statistical analysis skills and the ability to interpret model performance metrics critically
• Experience with data querying languages (e.g., SQL) and working with large datasets.

Preferred Qualifications:

• Experience in the healthcare domain (Provider, Payer, Life Sciences, HealthTech) and familiarity with healthcare data (EHR, claims, imaging, - omics)
• Experience evaluating third-party AI/ML solutions or working with MLaaS platforms.
• Familiarity with ethical AI principles, fairness, accountability, and transparency (FAT) in machine learning
• Experience with cloud-based ML platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform).
• Understanding of data governance, data privacy
• Strong communication skills, with the ability to explain complex technical findings to diverse audiences

Regards,

Ollie