Senior Data Scientist - W2 Only

About this Position:

Job Title: Senior Data Scientist

Responsibilities:

• Establish a development framework for the PoC, including task breakdowns, milestones, deliverables, risks, and mitigation plans.

• Participate in analyzing current intake processes, decision workflows, and resource allocation to identify and prioritize shortcomings.

• Lead the design of a new intake approach using NLP and machine learning to address identified gaps.

• Define functional components, evaluate architectural/computational trade-offs, and assess risks (technical, schedule, security).

• Evaluate and select appropriate data sources (existing, sample, simulated) and document the final approach for transparency.

• Develop and lead design reviews, assessing functional effectiveness, risks, data usage, and testing/demonstration methods.

• Oversee PoC implementation with regular updates, risk mitigation, and support for resolving technical/administrative issues.

• Conduct demo sessions, gather stakeholder feedback, contribute to roadmap development, and support Agile processes for project success.

Required Skills/Experience:

• 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 client's 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.