TypeScriptMachine LearningNLPNatural Language ProcessingData Engineering
About this role
Role Overview
Seeking a Senior Data Scientist to support a proof-of-concept demonstration using natural language processing and other machine learning methods to improve the intake process.
Oversee and support the development, implementation, and testing of statistical models, integration of NLP, and refinement and testing of the prototype.
Work closely with State stakeholders and technical team members to ensure the quality of the results and that the derived methods are transparent, statistically sound, relevant, and documented.
Establish a framework for the execution of technical tasks within the proof-of-concept.
Identify critical milestones related to information, receipt of data, testing, and delivery.
Identify key risk factors and means of mitigation.
Lead the development of a new intake process that leverages natural language processing and other machine learning algorithms.
Document the final approach for transparency.
Create the framework for the design review process.
Requirements
Bachelor’s, Master’s, or Ph.D. in computer science, mathematics, engineering, physics, or related field.
Have participated in US Federal Gov’t data science programs requiring TS/SCI clearance, delivering solutions requiring the combination of geospatial disciplines and pattern of life, and Social network connections.
Data engineering expertise, with demonstrable experience custom building programs processing in excess of 700 Million records in less than :30min, on a highly frequent, reoccurring basis.
Proven expertise working with CCWIS data attributes to predict child welfare outcomes, including but not limited data attribute selection, data clean up and statistical tuning.
Extensive knowledge of statistical algorithms, machine learning, and adaptive systems.
Prior history of designing and building machine learning algorithms from the ground up.
Experience with making technical trade-offs between algorithmic approaches. based on collective errors, computational time, scalability, and outcomes.
Prior success in developing optimal non-rule-based decision-making systems where the inputs are stochastic.
Successful history of converting social processes and human decision-making into computational models that yield improved results.