Healthcare Data Scientist - Machine Learning & Clinical Analytics

Al Futtaim Private Company (LLC); United Arab Emirates; On-site; AED 167,000 - 257,000; Full time

Benefits offered by this job

Healthcare data projects experience

Job summary

Al Futtaim Private Company (LLC) is seeking a data scientist to build and evaluate predictive ML models for healthcare use cases. You will analyze diverse healthcare data (EHR, claims, pharmacy, labs) and apply techniques such as logistic regression, random forests, and XGBoost, while ensuring data privacy and governance.

Collaboration with clinicians and stakeholders is essential. The role requires 2–5 years in data science, advanced Python/R, and strong SQL skills to support analytics across

Qualifications

• Bachelor’s degree in a quantitative discipline as listed in the job description.
• 2–5 years’ experience in data science.
• Experience with healthcare datasets and health informatics.
• Hands-on experience building predictive models and ML solutions.
• Advanced Python or R for data science and ML modeling.
• Advanced SQL for complex data extraction and cohort building.
• Experience with EHR/claims/pharmacy/lab data and healthcare operations.
• Understanding of ICD-10/11, CPT, SNOMED CT, LOINC, HL7, or FHIR.
• Ability to communicate outputs to clinical, operational, finance, and leadership teams.

Responsibilities

• Build train test and evaluate predictive machine learning models for healthcare use cases
• Develop models that support clinical risk prediction patient outcomes utilisation readmission risk claims analytics revenue cycle optimisation and care pathway improvement
• Analyse and model healthcare datasets including EHR clinical claims pharmacy laboratory patient activity and healthcare operational data
• Apply statistical and machine learning techniques such as logistic regression survival analysis classification clustering random forest XGBoost and other predictive modelling methods
• Prepare analytical datasets using advanced SQL including cohort building joins window functions longitudinal patient data feature engineering and data quality checks
• Work with healthcare data standards and coding systems such as ICD-10 11 CPT SNOMED CT LOINC HL7 and FHIR
• Support clinical decision support predictive analytics healthcare AI and data governance initiatives
• Collaborate with clinicians and operational stakeholders to translate data science outputs into practical healthcare workflows
• Evaluate model performance using appropriate metrics such as AUC precision recall F1 score sensitivity specificity calibration and explainability methods
• Ensure healthcare data privacy ethical AI use and compliance with relevant healthcare data governance requirements

Skills

Python/R

SQL

Predictive modelling

Model evaluation

Communicating with stakeholders

Healthcare data understanding

Education

Bachelor’s Degree in Data Science, Computer Science, Statistics, Biostatistics, Biomedical Engineering, Health Informatics, or a related quantitative discipline

Tools

Power BI

Tableau/Plotly/Dash

Job description

• Build train test and evaluate predictive machine learning models for healthcare use cases
• Develop models that support clinical risk prediction patient outcomes utilisation readmission risk claims analytics revenue cycle optimisation and care pathway improvement
• Analyse and model healthcare datasets including EHR clinical claims pharmacy laboratory patient activity and healthcare operational data
• Apply statistical and machine learning techniques such as logistic regression survival analysis classification clustering random forest XGBoost and other predictive modelling methods
• Prepare analytical datasets using advanced SQL including cohort building joins window functions longitudinal patient data feature engineering and data quality checks
• Work with healthcare data standards and coding systems such as ICD-10 11 CPT SNOMED CT LOINC HL7 and FHIR
• Support clinical decision support predictive analytics healthcare AI and data governance initiatives
• Collaborate with clinicians and operational stakeholders to translate data science outputs into practical healthcare workflows
• Evaluate model performance using appropriate metrics such as AUC precision recall F1 score sensitivity specificity calibration and explainability methods
• Ensure healthcare data privacy ethical AI use and compliance with relevant healthcare data governance requirements urrently residing in the UAE.

• Bachelor’s Degree in Data Science, Computer Science, Statistics, Biostatistics, Biomedical Engineering, Health Informatics, or a related quantitative discipline.
• Minimum 2–5 years’ experience in data science.
• Minimum 2 years’ experience working with healthcare datasets, clinical analytics, health informatics, healthcare AI, health insurance data, or health-tech data.
• Proven hands‑on experience building predictive models or machine learning solutions.
• Advanced Python or R experience for data science, machine learning, statistical modelling, feature engineering, and model evaluation.
• Advanced SQL experience for complex extraction, analytical dataset creation, cohort building, and healthcare data preparation.
• Experience working with healthcare datasets such as EHR, clinical, claims, pharmacy, laboratory, patient activity, or healthcare operational data.
• Understanding of healthcare coding or interoperability standards such as ICD-10/11, CPT, SNOMED CT, LOINC, HL7, or FHIR.
• Ability to communicate technical outputs clearly to clinical, operational, finance, and leadership stakeholders.

Highly Desirable

• Master’s Degree or PhD in Health Data Science, Biostatistics, Computational Biology, Data Science, or a related field.
• Experience with UAE healthcare data environments, DHA regulations, NABIDH, Malaffi, or healthcare data residency requirements.
• Experience working within a hospital, clinic network, healthcare group, health insurance provider, health‑tech company, clinical analytics team, or health informatics environment.
• Experience with clinical decision support systems.
• Experience with SHAP, LIME, or other model explainability methods.
• Experience with Power BI, Tableau, Plotly, or Dash to communicate data science outputs to leadership.