
Data Scientist
- Bangalore, Karnataka
- Permanent
- Full-time
Practice: Healthcare
Level: Associate
Location: BangaloreKey Responsibilities:
- Analyze large-scale healthcare claims and transaction data (charges, payments, denials, write-offs, etc.)
- Develop and implement predictive models, such as XGBoost, Random Forest, or Logistic Regression, for denial prediction, transaction classification, and payment forecasting
- Apply unsupervised learning techniques (e.g., clustering, MBA) to detect denial root causes, payer patterns, and operational inefficiencies.
- Identify trends and anomalies to support root cause analysis in denials and underpayments
- Build and validate machine learning models likw classification, forecasting, clustering for Denial prediction and pattern recognition, Cash collection forecasting and Write-off root cause analysis
- Use tools such as Python (scikit-learn, XGBoost, pandas), SQL, and AWS services like SageMaker, Athena
- Translate business problems into machine learning problems and deliver solutions with clear, measurable outcomes.
- Collaborate with business stakeholders to define use cases and translate them into analytical models and interactive insights
- Work with large datasets from AWS Redshift, S3, Oracle, SageMaker, Excel, and other sources to preprocess and prepare training datasets
- Provide statistical analysis and model validation to ensure accuracy and reliability on unseen RCM data
- Automate and replace manual Excel-based reports with AI-powered analytics and decision support tools
- Collaborate with data visualization teams to integrate model outputs into business-friendly dashboards using AWS QuickSight or Power BI
- Assist in integrating models into production environments and monitoring performance
- Work closely with domain experts, operations leaders, and client teams to translate business questions into analytical solutions
- Participate in brainstorming sessions for new use cases and innovations
- 5+ Yrs of experience in analytics and Data science
- Proficient in Python and SQL for data transformation and model building
- Hands-on experience with supervised and unsupervised ML techniques, including clustering, classification, and association rule mining
- Exposure to statistics, hypothesis testing, and model performance evaluation techniques (e.g., ROC-AUC, precision/recall, F1)
- Experience with AWS tools such as SageMaker, Redshift, Athena, S3; familiarity with Snowflake is a plus
- Preferred knowledge on Revenue Cycle Management
- Exposure to Python, SQL and data querying for extracting insights and Excel formulae
- Good communication skills and ability to work with business teams.
- Eagerness to learn cloud-based data tools (AWS, S3, Redshift, Snowflake, etc.)