Data Scientist

Job Title: Data Scientist.

Job Summary:

We are seeking a Data Scientist with 3+ years of experience deploying ML models into production. This role will focus on designing, building, and optimizing ML models while ensuring their seamless integration into our healthcare systems. Data Scientists will drive data-driven innovation and improve our operational efficiency, patient experience, and overall service delivery.

Job Responsibilities:

• Develop, implement, and optimize machine learning models to address various challenges in healthcare, including predictive modeling, diagnostics, and operational optimization.
• Analyze complex healthcare datasets, including patient records, lab results, insurance claims, and operational metrics, to derive actionable insights.
• Work with deployment tools and frameworks such as Docker, Kubernetes, MLflow, or TensorFlow Serving to operationalize ML models.
• Collaborate with cross-functional teams, including clinicians, lab technicians, and insurance analysts, to identify opportunities for leveraging data science and ML to enhance decision-making.
• Design, build, and maintain scalable data pipelines and workflows to support ML model development and deployment.
• Conduct exploratory data analysis to identify trends, anomalies, and opportunities for process improvements.
• Evaluate and fine-tune ML models to improve performance and ensure compliance with regulatory standards.
• Create detailed documentation for models, experiments, and workflows, ensuring reproducibility and transparency.
• Stay updated on the latest trends and advancements in machine learning and healthcare data analytics.

Job Requirements:

• 3+ years of hands-on experience deploying machine learning models into production.
• Knowledge of statistical concepts such as hypothesis testing, regression analysis, ANOVA, experimental design, and probability theory.
• Experience with tools and frameworks for production deployment, including Docker, Kubernetes, MLflow, TensorFlow Serving, AWS SageMaker, or other similar tools.
• Strong understanding of MLOps principles, including CI/CD pipelines for ML, model versioning, and monitoring.
• Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, Statistics, or a related field. A Ph.D. is a plus.
• Data visualization skills using libraries like Matplotlib, Seaborn, or ggplot.
• Strong foundation in machine learning algorithms, including supervised, unsupervised, and reinforcement learning.
• Understanding of machine learning algorithms, supervised and unsupervised learning, feature engineering, model selection and evaluation, and hyperparameter tuning is essential.
• Proficiency in programming languages such as Python, and experience with ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
• Experience with SQL and working with structured and unstructured data in large-scale environments.
• Familiarity with healthcare data standards (e.g., HL7, FHIR) and regulatory considerations such as HIPAA or GDPR.
• Proven ability to preprocess and clean complex datasets for machine learning applications.
• Strong problem-solving skills and the ability to communicate complex concepts effectively to non-technical stakeholders.
• Skills in handling and analyzing large datasets, including data cleaning, wrangling, transformation, and exploratory data analysis (EDA) using tools like pandas.