Radiant Digital

Data Scientist (Child Welfare Program)

⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Scientist (Child Welfare Program) with a 6-month contract, remote in Jackson, MS. Key skills include expertise in machine learning, data engineering, and NLP, with experience in child welfare data and federal government programs requiring security clearance.
🌎 - Country
United States
πŸ’± - Currency
$ USD
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πŸ’° - Day rate
Unknown
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πŸ—“οΈ - Date
March 20, 2026
πŸ•’ - Duration
More than 6 months
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🏝️ - Location
Remote
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πŸ“„ - Contract
Unknown
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πŸ”’ - Security
Yes
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πŸ“ - Location detailed
United States
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🧠 - Skills detailed
#Data Science #Data Engineering #ML (Machine Learning) #Computer Science #Mathematics #Scala #NLP (Natural Language Processing)
Role description
Job Title: Data Scientist Location: Remote- Jackson, MS Duration: 6 Months plus Job Description: The Senior Data Scientist will be responsible for overseeing and supporting the development, implementation, and testing of statistical models, integration of NLP, and refinement and testing of the prototype. In addition, algorithmic trade-offs will be evaluated, and guidance provided to ensure the State’s objectives are satisfied. The Senior Data Scientist will 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. REQUIRED SKILLS: β€’ Bachelor’s, Master’s, or Ph.D. in computer science, mathematics, engineering, physics, or related field. β€’ 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. β€’ 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. β€’ 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. β€’ Extensive knowledge of statistical algorithms, machine learning, and adaptive systems. β€’ Prior history of designing and building machine learning algorithms from the ground up. β€’ Experience with making technical trade-offs between algorithmic approaches. based on collective errors, computational time, scalability, and outcomes. β€’ Prior success in developing optimal non-rule-based decision-making systems where the inputs are stochastic. β€’ Successful history of converting social processes and human decision-making into computational models that yield improved results.