Overview
We’re seeking a Machine Learning Data Scientist with deep expertise in healthcare claims data to design, build, and deploy advanced analytics and machine learning modeling solutions. In this role, you’ll transform complex administrative and clinical datasets into actionable insights that improve cost efficiency, care quality, and operational performance across the healthcare ecosystem.
You’ll collaborate with data engineers, clinicians, and product teams to develop predictive models, optimize workflows, and support strategic decision‑making. This position is ideal for someone who thrives at the intersection of data science, healthcare operations, and modern machine learning.
Key Responsibilities
Machine Learning & Advanced Analytics
Develop, train, and deploy ML models for use cases such as:
Claims cost prediction and utilization forecasting
Fraud, waste, and abuse detection
Risk adjustment and member stratification
Provider performance and network optimization
Apply modern ML techniques including gradient boosting, deep learning, NLP, and probabilistic modeling.
Capable of applying advanced predictive analytics to correlate disparate datasets and events and derive business value.
Build scalable pipelines for feature engineering, model training, validation, and monitoring.
Healthcare Claims Expertise
Analyze and interpret medical, pharmacy, and dental claims (CPT/HCPCS, ICD‑10, DRG, NDC).
Translate domain knowledge into meaningful features and model strategies.
Cross‑Functional Collaboration
Partner with clinicians, product managers, and business stakeholders to define problems and measure outcomes.
Communicate complex analytical findings in clear, actionable terms.
Required Qualifications
Strong proficiency in Python and ML libraries (scikit‑learn, XGBoost, TensorFlow/PyTorch).
Hands‑on experience with healthcare claims datasets and coding systems.
Solid understanding of statistical modeling, machine learning algorithms, and data mining techniques.
Strong knowledge and expertise working with SQL.
Ability to translate business needs into analytical solutions.
Must have demonstrated the ability to solve complex problems with minimal direction.
Preferred Qualifications
Experience with NLP applied to clinical notes or unstructured healthcare data.
Familiarity with actuarial concepts, risk scoring, or value‑based care models.
Familiarity deploying models into production (MLOps, CI/CD).
Background in health economics, epidemiology, or biostatistics.
Prior work with FHIR, HL7, or interoperability standards.
SmartLight Analytics was formed by a group of industry insiders who wanted to make a meaningful impact on the rising cost of healthcare. With this end in mind, SmartLight combats fraud, waste, and abuse in healthcare through our proprietary data analysis and model development. Requiring the bare minimum in employer involvement, our process works behind the scenes to save money without interrupting employee benefits or requiring employee behavior changes.
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What We Do
SmartLight Analytics was formed by a group of industry insiders driven to make a meaningful impact on the rising cost of employee healthcare. Using our statistical, clinical, fraud detection, coding and claims expertise we deliver the most complete wasteful spend reduction solution directly to self-funded employers. SmartLight utilizes proprietary inferential analytics customized to your employee population, followed by expert clinical review on 100% of your medical claims. Our team partners with your TPA to implement solutions resulting in a lower per employee healthcare spend. We let your data tell us where to look without any preconceived notions about what the errors are beforehand. Our approach is low-touch and involves zero employee involvement. SmartLight consistently delivers a higher ROI compared to other cost reduction solutions on the market.
