Senior AI​/ML Data Scientist — Remote TS​/SCI

State of Mississippi - Sr AI/ML Data Scientist (US Federal Gov’t exp with TS/SCI clearance needed) - MS - MDCPS- 156573 (Remote)

Closing Date: 3/23/2026

100% remote

Required Skills:

• 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.

Job Description :

The Mississippi Department of Child Protective Services seeks 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.

This work is critical to demonstrate the potential of the latest technology to improve the lives of children at risk.

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.

Key Responsibilities

• Create a Development Framework

o Establish a framework for the execution of technical tasks within the proof-of-concept. The framework will

consist of task breakouts, milestones, and deliverables

o Identify critical milestones related to information, receipt of data, testing, and delivery.

o Identify key risk factors and means of mitigation.

• Current Processes & Technology

o Participate in critical discussions involving current intake workflows, how decisions are made based on

information from the intake process, and the allocation of State labor.

2

o Contribute to the identification of intake process shortcomings and prioritize based on their impact on children

and State resources.

• Devise New Intake Approach Using New Technologies

o Lead the development of a new intake process that leverages natural language processing and other machine

learning algorithms.

o Identify the functional blocks and reconcile their contributions to solving the prioritized shortcomings.

o Evaluate architectural and computational implementation trade-offs for each functional block. The evaluation

should consider risk from the standpoints of technical, schedule, and security.

o Evaluate trade-offs of using different data sources, including existing systems, sample data, simulated data, or

other alternatives.

o Document the final approach for transparency.

• Design Review(s)

o Create the framework for the design review process.

o Lead the design review and evaluate

The functional design with respect to resolving prioritized intake process shortcomings, and the impact on

children and State resources.

Technical, schedule, data security, and other risk factors.

Source of data and its usefulness in demonstrating the efficacy of the approach.

Proposed methods of test and demonstration.

o Documentation of the process…