Job Description
Location
Connecticut (Hybrid: Telework with required in‑person meetings)
Work Schedule
Monday–Friday, 7:30 AM – 4:00 PM
Project / System Overview
Connecticut’s Criminal Justice Information System (CJIS) is the integrated framework through which state and local criminal justice agencies collaborate to improve communication, data sharing, and decision-making across the justice system. CJIS supports the effective management of crime and offender data, enabling agencies to operate with greater cohesion, transparency, and efficiency.
As part of ongoing Criminal Justice Information Sharing (CISS) initiatives, CJIS-CT is developing a new product—CISS Analytics—in accordance with CISS legislation. The primary objective of CISS Analytics is to equip the CJIS Analytics Subcommittee with meaningful, data-driven insights to inform policy decisions, legislative changes, and strategic initiatives that serve the interests of Connecticut residents and state agencies.
To support this mission, CJIS-CT is seeking a Data Scientist capable of transforming complex data into actionable insights and scalable machine learning solutions. This role will collaborate closely with CJIS SQL DBAs, business stakeholders, source data owners, and the CJIS Analytics Subcommittee to enable informed, evidence-based decision-making.
Scope of Work
The CJIS Data Scientist position is essential to meeting newly identified analytic and modeling needs in support of the CJIS Analytics Subcommittee. The successful candidate will deliver measurable impact through data-driven initiatives, develop and deploy reliable analytical and machine learning solutions, support key performance indicators (KPIs), and help build a scalable and sustainable data science infrastructure within CJIS.
Key Responsibilities
- Partner with business users and stakeholders to translate legislative, policy, and operational challenges into data science problems and analytical solutions
- Collect, clean, validate, and pre-process large structured and unstructured datasets
- Develop, validate, and assist in deploying machine learning models across various use cases
- Perform statistical analysis, hypothesis testing, and exploratory data analysis
- Design, implement, and analyze A/B and other controlled experiments
- Build predictive and prescriptive models to support decision-making
- Communicate complex analytical insights clearly to both technical and non-technical audiences
- Collaborate with data engineers, DBAs, and architecture teams to operationalize models
- Monitor model performance and assist with retraining and tuning as required
- Contribute to data science standards, documentation, and best practices
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related discipline
- Minimum of 2+ years of professional experience in data science, analytics, or machine learning (experience level may vary based on seniority)
- Strong proficiency in Python, including libraries such as pandas, NumPy, and scikit-learn
- Hands-on experience with SQL and relational databases
- Solid foundation in statistics and probability theory
- Experience developing and validating machine learning models (classification, regression, clustering, and neural networks)
- Experience using data visualization tools such as Tableau, Power BI, or similar platforms
- Proven ability to work with large and complex datasets
Preferred Skills & Experience
- Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
- Experience working with cloud platforms such as AWS or Azure
- Familiarity with MLOps tools and practices (e.g., MLflow, Airflow, Docker)
- Experience deploying models across multiple SDLC environments
- Domain experience within criminal justice, public safety, or government agencies is highly desirable
- Strong analytical and problem-solving skills
- Excellent communication, documentation, and presentation abilities
- Ability to translate complex analytical outcomes into meaningful business and policy insights
- Self-motivated, proactive, and results-oriented with a strong sense of ownership
- Collaborative and team-driven approach to problem-solving
Administrative Considerations
- Work Environment: Hybrid work model including telework with required in-person meetings
- Supervision & Resources: State-provided workstation; role supervised by the CJIS Program Manager
- Security & Privacy Requirements:
- Successful completion of a background investigation
- Execution of confidentiality and data protection agreements
- Ownership of Work Product:
- All work products and deliverables are the property of the CJIS Governing Board
