Sr. Database Architect

Job Title:Data Scientist(Healthcare ML & Analytics Mid/Senior Level)

Location: Onsite-full time$80K to $130K must be authorized to work in USA no visa sponsorship available

Company Overview:Lifekind Health is a leading provider of transdisciplinary pain management patient care and is launching Savas Software AI-enabled next generation healthcare software. We combine clinical excellence with advanced machine learning analytics engineering and intelligent automation to transform how care is delivered.

We are committed to providing exceptional patient care and dedicated to improving healthcare delivery through innovative technology. We are building the data and ML foundation that will power the future of personalized proactive and value-based healthcare.

Position Overview:

We are seeking a technically strong impact-driven Data Scientist with experience building ML-based predictive products and advanced analytics in real-world this role you will work with diverse and complex healthcare datasetsEHR scheduling billing claims structured & unstructured clinical datato design train and deploy machine learning models that directly influence patient care operational performance and clinical efficiency.

This is a high-ownership hands-on role where youll help shape our intelligent data platform build production-ready features experiment with models and collaborate with engineering teams to push ML systems into production.

If you enjoy solving messy high-impact healthcare problems using advanced ML this role is for you.

Key Responsibilities

Machine Learning & Predictive Analytics

• Develop and deploy ML models that power key products such as:
  • Procedure Appropriateness
  • Patient no-show prediction
  • Appointment optimization
  • Clinical risk stratification
  • Patient adherence forecasting
  • Provider utilization and throughput prediction
• Perform feature engineering using clinical operational and financial data.
• Experiment with algorithms (tree-based models GLMs ensemble methods NLP deep learning where appropriate).
• Evaluate models using rigorous statistical and ML performance metrics.
• Collaborate with ML engineering to productionize models on Azure

Data Analysis & Insights

• Conduct exploratory data analysis (EDA) on EHR scheduling billing and procedural data to uncover trends biases and quality issues.
• Translate clinical guidelines and workflows into computable data-driven logic.
• Generate actionable insights that drive clinical and operational decision-making.

Data Engineering & Feature Pipelines

• Build and maintain reproducible feature pipelines in an Azure ecosystem (ADLS ADF Azure Functions).
• Transform raw healthcare data into modeling-ready datasets (structured unstructured).
• Implement data validation quality checks and scalable transformation logic.
• Collaborate with Data Engineering to ensure high-quality well-governed data flows.

NLP & Unstructured Data (Nice-to-Have but Valuable)

• Apply basic NLP techniques to extract signal from clinical notes and operational text.
• Explore entity extraction rule-based labeling embedding-based features etc.

Visualizations & Storytelling

• Create dashboards and data visualizations using Power BI or Python to communicate insights.
• Present findings and recommendations to clinicians operations leaders and executives.

Technical Environment (Azure ML & Analytics)

Youll work within a modern Azure-based ML and analytics stack including:

• Core Languages: Python SQL
• Libraries & Frameworks: Scikit-learn XGBoost LightGBM Pandas NumPy NLP libraries
• Visualization: Power BI Plotly Matplotlib Seaborn

Youll have the opportunity to help design and scale the ML architecture as the platform grows.

Qualifications

• 2 or more years of experience in data science machine learning or applied analytics.
• Strong Python advanced SQL skills for data manipulation modeling and EDA.
• Experience developing and evaluating ML models in real-world environments.
• Experience with healthcare datasets (EHR claims clinical notes billing scheduling) is a strong advantage.
• Strong understanding of feature engineering statistical methods and model validation.
• Ability to clearly communicate technical concepts to non-technical stakeholders.

Preferred Skills

• Experience with Azure Data Factory Azure Functions Azure Open AI
• Experience deploying or operationalizing ML models (Azure ML ideal).
• Exposure to NLP for clinical text.
• Masters degree in Data Science CS Statistics Biomedical Informatics or related field.
• Familiarity with HIPAA PHI handling and healthcare data governance.

What Success Looks Like

• Production-ready ML models that drive measurable improvements in clinical operations.
• High-quality datasets features and reproducible pipelines powering our AI platform.
• Actionable insights that influence patient outcomes and reduce operational friction.
• Ability to independently drive complex data projects end-to-end with minimal supervision.

What Sets Us Apart:
Our team is mission-driven and united by a common purpose: healing humanity through a revolutionary transdisciplinary model. The model addresses the quadruple aim of healthcare: enhancing patient experience improving patient health reducing healthcare costs and increasing employee satisfaction. We thrive on collaboration and each member is a self-starter empowered to unblock themselves and move forward with confidence. If you are passionate about impacting healthcare enjoy taking initiative and are comfortable with ambiguity youll find a great fit with us.