Head of Fraud & Risk Data Science
Sardine$220,000 - $270,000/year
Full Time
Senior (5 to 8 years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
UnknownVisa
HealthcareIndustries
Requirements
- MSc or higher degree in data science/analytics, statistics, mathematics, engineering related field
- 7+ years hands-on data science experience at a company where health outcome performance were critical to the mission, preferably a healthcare insurance company
- Expert in conducting data mining in the healthcare insurance industry and claims-related experience and knowledge of coding, reimbursement, and claims processing policies
- Knowledge of the law and regulations as it relates to FWA investigations and familiar with CMS Medicare and Medicaid policies
- Proficient in current modeling approaches, standard scientific methods for intervention based analysis, trend identification, causal inference, and pattern detection
- Strong experience using a variety of data mining/data analysis methods, utilizing a variety of analytic tools, building and implementing models, creating algorithms and running simulations
- Experience with programming languages (Python, PySpark, Scala and/or Spark SQL)
- Experience with graph database managing clinical concepts (big plus)
Responsibilities
- Drive strategic data science initiatives that focus on healthcare fraud, waste and abuse including identification, investigation, validation, escalation and prevention
- Perform proactive fraud, waste and abuse detection analysis derived from various monitoring based data sources
- Serve as a subject matter expert on identifying and discovering claim and authorization outliers and interpret and analyze data to accurately assess and demonstrate key insights into trends and opportunities
- Independently lead in-depth analyses of a variety of healthcare and product data sources to investigate suspicious activities, and prepare detailed reports of findings
- Stay up-to-date with the latest industry trends, regulatory changes, and emerging fraud schemes to enhance detection strategies, maintain meticulous methodology documentation, and present findings or recommendations to leadership
- Translate business needs from stakeholders into data science technical requirements and translate data science results back to business and clinical units
- Present information using data visualization techniques and propose solutions and strategies to business challenges
Skills
Key technologies and capabilities for this role
Questions & Answers
Common questions about this position
What education and experience are required for this Lead Data Scientist role?
Candidates need an MSc or higher degree in data science/analytics, statistics, mathematics, or engineering-related field, plus 7+ years of hands-on data science experience in a company where health outcome performance was critical, preferably in healthcare insurance.
What key technical skills are needed for this position?
Expertise in data mining within the healthcare insurance industry, proficiency in modeling approaches, scientific methods for intervention analysis, trend identification, causal inference, pattern detection, and experience with data analysis tools, model building, algorithms, and simulations are required.
What is the salary or compensation for this role?
This information is not specified in the job description.
Is this position remote or does it require office work?
This information is not specified in the job description.
What growth opportunities does this role offer?
This is a position that offers the ability to make a substantial impact on the company with rapid growth opportunities as part of a growing data science team within the technology organization.
Cohere Health
Automates prior authorization in healthcare
About Cohere Health
Cohere Health improves the prior authorization process in healthcare, which requires providers to get approval from health plans before delivering certain services. Their platform, Cohere Unify, automates and streamlines these requests by integrating with electronic medical records and using technologies like optical character recognition and machine learning. This approach allows for faster decision-making and reduces delays in patient care. The goal is to make the prior authorization process more efficient, benefiting healthcare providers, health plans, and patients.
Boston, MassachusettsHeadquarters
2019Year Founded
$103.1MTotal Funding
LATE_VCCompany Stage
AI & Machine Learning, HealthcareIndustries
501-1,000Employees