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. "No phone calls please." "We are an equal opportunity employer and do not discriminate based on race, color, religion, gender, sexual orientation, national origin, age, disability, citizenship or any protected status."