REMOTE - Senior Data Scientist at Piper Companies
Fairfax, Virginia, USA -
Full Time


Start Date

Immediate

Expiry Date

04 Dec, 25

Salary

110000.0

Posted On

04 Sep, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

R, Github, Sql, Docker, Data Science, System Performance, Testing, Python

Industry

Information Technology/IT

Description

Zachary Piper Solutions is seeking a Senior Data Scientist to join a Federal Program located on a remote schedule. The Senior Data Scientist will perform scientific work associated with the analytical, statistical, and programming skills to collect, analyze, and interpret large data sets.

QUALIFICATIONS OF THE SENIOR DATA SCIENTIST:

  • Bachelor’s Degree in data science, or related field
  • Proven experience developing machine learning models and engineering features using Python, PySpark, SQL, R, Databricks, Machine learning with regularization models (especially LASSO), and other packages in Amazon SageMaker AI
  • Able to create complex queries in SQL for testing and data analysis
  • Experience developing ML pipelines and with ML Operations
  • Experience with Model Tuning and Governance
  • Experience with the following tools: MLFlow, GitHub, Docker
  • Must be able to work with engineering teams to source the right data, evaluate system performance
  • Data Science experience with healthcare and/or claims analysis
Responsibilities
  • Perform scientific work associated with the analytical, statistical, and programming skills to collect, analyze, and interpret large data sets
  • Use large data sets to find opportunities for product and process optimization
  • Use models to test the effectiveness of different courses of action.
  • Use predictive modeling to increase and optimize customer experience, efficiencies, process improvements, and other business outcomes
  • Mine and analyze data using a variety of data tools
  • Build and implement models using/creating algorithms and creating/running simulations
  • Develop processes and machine learning-based tools to monitor and analyze model performance and data accuracy
  • Communicate analytic findings and recommendations to non-technical stakeholders, verbally and through written reports
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