Job Description

Data Scientist Recommender Systems (Healthcare)

Location: Remote

Job Type: Contract

Experience: 3-8 years

Industry: Healthcare, HealthTech, Digital Health, Pharma

About the Role:

We are looking for a Data Scientist specializing in Recommender Systems to develop personalized healthcare recommendation models that improve patient outcomes, enhance clinical decision-making, and optimize user engagement on digital health platforms. You will work on AI-driven recommendation engines for medication adherence, treatment pathways, clinical decision support, and personalized wellness programs.

Key Responsibilities:

• Design and develop AI-powered recommendation systems to personalize treatment plans, drug recommendations, preventive care, and wellness suggestions.
• Implement collaborative filtering, content-based filtering, and hybrid recommendation models to provide tailored healthcare recommendations for patients, doctors, and care providers.
• Apply Natural Language Processing (NLP) and embeddings (BERT, BioBERT, Med-BERT) to extract insights from clinical notes, medical literature, and EHRs (Electronic Health Records).
• Work with graph-based recommendations (Knowledge Graphs, Graph Neural Networks) to enhance precision in disease diagnosis, drug interactions, and treatment suggestions.
• Develop personalized patient engagement models using predictive analytics and AI to improve adherence to medications and health regimens.
• Optimize recommendation systems using evaluation metrics such as NDCG, MRR, MAP, Precision@K, and Recall@K.
• Collaborate with healthcare providers and clinical experts to validate and interpret model outputs for patient-centric decision-making.
• Ensure compliance with HIPAA, GDPR, and other healthcare data privacy regulations while handling sensitive patient information.
• Deploy scalable recommendation models using AWS/GCP/Azure, MLflow, TensorFlow Serving, Docker, and Kubernetes.
• Research and apply state-of-the-art healthcare AI techniques, including Federated Learning for privacy-preserving recommendations.

Required Skills & Qualifications:

• 3+ years of experience in Machine Learning, Deep Learning, and Recommender Systems in a healthcare or digital health setting.
• Strong programming skills in Python, SQL, and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
• Experience working with medical datasets such as EHRs, claims data, clinical trial data, and genomic data.
• Solid knowledge of collaborative filtering (ALS, SVD, NMF), content-based filtering, and hybrid models.
• Understanding of healthcare ontologies (SNOMED-CT, ICD-10, RxNorm, UMLS) for structured recommendation models.
• Proficiency in Big Data technologies such as Apache Spark, Hadoop, and Kafka for large-scale patient data processing.
• Hands-on experience with ML model deployment (Docker, Kubernetes, MLflow, Airflow).
• Strong understanding of evaluation techniques for recommendation models, including real-world testing and A/B testing in healthcare settings.

Preferred Qualifications:

• Master's or PhD in Computer Science, AI, Machine Learning, Bioinformatics, or a related field.
• Experience in digital therapeutics, personalized medicine, or AI-driven clinical decision support systems.
• Familiarity with Federated Learning to ensure patient data privacy and compliance with HIPAA & GDPR.
• Experience working in HealthTech, Pharma, Hospitals, or Medical AI startups.
• Knowledge of FHIR, HL7, and other healthcare interoperability standards for integrating AI recommendations into clinical workflows.

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