Alphident Technologies, Inc.

Senior Data Scientist

Remote, US$100,000-$120,000Posted 11 days ago

Job Description

Title

: Senior Data Scientist

Work location

: Remote

Agency

: Small Business Administration (SBA)

Duration of contract

: 5 years

Job Responsibilities

  • Design, develop, and implement advanced statistical and machine learning models targeted at identifying fraud, improper payments, or non-compliance within programs.
  • Integrate and scale natural language processing (NLP) methods to parse, clean, and analyze large corpora of unstructured or semi-structured text data, utilizing OCR, semantic similarity algorithms, and large language models as needed.
  • Assist with data analytic products by providing highly skilled and authoritative expertise on analytic methods.
  • Perform data quality analysis on source tables to identify any abnormalities or inconsistencies.
  • Review, maintain, and provide support using all existing fraud indicators.
  • Develop and refine repeatable processes for efficiently combining and analyzing large data sources (including relational tables, structured and unstructured datasets).
  • Collaborate closely with criminal investigators to determine and execute strategies to support cases; proactively address and communicate potential data quality issues and adapt analysis to shifting case needs; adhere closely to federal rules of criminal procedure governing protected information
  • Utilize indicators of potential fraud, improper payments, or non-compliance to develop and test predictive models.
  • Assist in developing case leads for investigations based on model outcomes.
  • Coordinate with the Data Engineering Team to ensure system architecture is designed to efficiently support machine learning models.
  • Provide detailed documentation of all methodology, test models, and production models; maintain records in compliance with criminal evidentiary requirements
  • Develop visualizations and dashboards to demonstrate methodological choices, outcomes, and predictive capabilities of machine learning models; iteratively solicit and integ...

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