Senior Data Scientist

Job Description Senior Data Scientist to support a proof-of-concept demonstration using natural language processing and other machine learning methods to improve the intake process. This work is critical to demonstrate the potential of the latest technology to improve the lives of children at risk. Location & Contract Jackson, MS (Remote) – 5 months contract Key Responsibilities Create a Development Framework, establishing task breakouts, milestones, and deliverables. Identify critical milestones related to information, receipt of data, testing, and delivery. Identify key risk factors and mitigation strategies. Participate in discussions of current intake workflows, decision-making processes, and allocation of State labor. Lead development of a new intake process that leverages NLP and other machine learning algorithms. Identify functional blocks and reconcile their contributions to solving prioritized shortcomings. Evaluate architectural and computational trade-offs for each functional block, considering technical, schedule, and security risks. Evaluate trade-offs of using different data sources, existing systems, sample data, simulated data, or other alternatives. Document the final approach for transparency. Lead design reviews and evaluate the functional design in relation to impact on children, State resources, and risk factors. Oversee implementation of the prototype, conduct weekly status updates and gate reviews. Provide conference‑room support during a 3–4 day demonstration of the prototype. Capture stakeholder comments on technical aspects of the application. Contribute to a roadmap illustrating how the developed technology could be integrated into the State’s ecosystem. Contribute to the Agile development process to ensure project success. Qualifications Bachelor’s, Master’s, or Ph.D. in computer science, mathematics, engineering, physics, or a related field. Experience in US Federal Government data science programs requiring TS/SCI clearance and delivering solutions that combine geospatial disciplines, pattern‑of‑life, and social network analysis. History of designing and building machine learning algorithms from the ground up. Experience making technical trade‑offs between algorithmic approaches based on collective error, computational time, scalability, and outcomes. Success in developing optimal non‑rule‑based decision‑making systems with stochastic inputs. Ability to convert social processes and human decision‑making into computational models that yield improved results. Data engineering expertise, demonstrably building programs that process in excess of 700 million records in under 30 minutes on a highly frequent, recurring basis. Proven experience working with CCWIS data attributes to predict child welfare outcomes, including data cleanup and statistical tuning. Extensive knowledge of statistical algorithms, machine learning, and adaptive systems. #J-18808-Ljbffr