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Modeling SPSS Python Analyst
(Jobs in Windsor Mill, MD)
 
Requirement id 114354
Job title Analyst
Job location in WINDSOR MILL, MD
Skills required Data Analytics, Modeling, Data Mining, SPSS Python
Open Date 24-Feb-2021
Close Date
Job type Contract
Duration 6 Months
Compensation DOE
Status requirement not found
Job interview type ---
   Email Recruiter: coolsoft
Job Description Analyst: Data Analytics, Modeling, Data Mining, SPSS Python

We should submit only GC,GC - EAD, L2-EAD,H4-EAD and Citizens.

Job Description:

Client has an immediate need for a Mid-Level Predictive Modeling Analyst supporting a Leading Government Integrator in Woodlawn, MD. The qualified applicant will become part of a major healthcare fraud prevention project, supporting a leading Government Healthcare organization. The qualified applicant will assist in the development & validation of predictive models to prioritize evaluation of possible fraudulent providers and claims. The selected candidate will have knowledge of/experience with SPSS, R, Python, Data Modeling, Machine Learning, and Agile methodologies.

Location: Woodlawn, MD (Remote during COVID)

Duration: Temp to Perm

Compensation: Competitive based on education & years of relevant experience

Clearance: Able to obtain & maintain a positions of Public Trust

Responsibilities:

Analyze claims data and assist in the validation/development of requirements for sustainable predictive analytic models and edits
Works with technical staff to test fraud models & validate proper setup for production
Working with the team and client to understand data and client needs
Detect potential weakness and vulnerabilities susceptible to fraud
Attend working groups with customers, investigators, and other contractors to elicit and evaluate vulnerabilities
Prevent FWA using system edits, policy recommendations, and customer/user education
Assist with the analysis of vulnerabilities and data anomalies
Monitor claims and suspicious activities utilizing proven investigating techniques
Reporting and communicating regarding the effectiveness of fraud prevention activities
Communicate with internal and external sources related to potentially fraudulent activity
Provide input on new ways of identifying fraud as well as improving existing fraud models

Requirements:

Bachelors Degree in related field of study, and 3-5 years of Data Analytics / Predictive Modeling experience for enterprise-level systems.
Must have hands on experience in the following areas:
Predictive analytics / modeling
Data mining
Machine Learning
SPSS, R, Python
Validation / Unit Testing for Models
Hands on experience with / knowledge of Machine Learning techniques & application
Experience with Agile project methodologies (preferably SAFe)
Knowledge of Medicare & Medicaid claims coding, processing systems, healthcare enrollment/eligibility data, as well as healthcare claims data including CPT, ICD 9 and ICD 10 codes
Goal-oriented, eager to learn, and ability to get things done with a proactive approach
Exceptional communication skills both verbal & written, with end clients & key stakeholders

Preferred:

Experience with SPSS to R transformation efforts
Experience with Cloud environments such as AWS or Azure
Experience with Databricks
Knowledge of anti-fraud principles, Healthcare policy, & Legislation
Experience in fraud prevention related field(s), including fraud detection, investigation, forensic accounting, judiciary, and law enforcement
Knowledge of the Medicare FFS programs (coverage, payment rules and regulation and claims process).
Experience working with customers to understand data and decision needs.
Familiarity with one or more data analysis tools, data mining tools, and/or statistical packages.
 
Call 502-379-4456 Ext 100 for more details. Please provide Requirement id: 114354 while calling.
 
Other jobs in MD: Annapolis (1), Baltimore (34), Baltimore Maryland (1), Beltsville (3), Downtown Baltimore (1), Elkridge (1), Frederick (2), Hanover (1), North Baltimore (2), Owings Mills (1), Rockville (1), Sparks (2), Timonium (4), Woodlawn (7),
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