[Hiring] Data Scientist @Morgan & Morgan, P.A.

Role Description

Data Scientist - Remote Opportunity:

• Design, develop, and maintain predictive models that support strategic planning, staffing, financial forecasting, and operational decisions.
• Apply statistical modeling, machine learning, and quantitative techniques to forecast outcomes, identify key drivers, and evaluate scenarios.
• Develop simulation and optimization models to support resource allocation and operational decision-making.
• Build and maintain scalable analytical tools, automations, and production model deployments.
• Analyze complex operational and financial datasets to identify trends, risks, and opportunities.
• Evaluate and improve model performance through backtesting, validation, sensitivity analysis, and ongoing monitoring.
• Refine analytical methodologies by incorporating new data sources, testing assumptions, and improving model accuracy.
• Partner with Operations, Finance, Marketing, and Legal teams to translate business challenges into analytical solutions.
• Communicate model outputs, assumptions, and recommendations to stakeholders, including executive leadership.
• Leverage AI to accelerate development, automate workflows, and scale analytical capabilities.

Qualifications

• BS or MS in Statistics, Mathematics, Computer Science, Engineering, Data Science, or related quantitative field.
• 4+ years of experience in data science, predictive analytics, statistical modeling, or forecasting.
• Demonstrated experience developing and maintaining statistical or predictive models that influence business decisions.
• Strong proficiency in Python and SQL for data manipulation, statistical analysis, modeling, and automation.
• Experience with statistical and machine learning techniques such as regression, time series forecasting, classification, simulation, or optimization.
• Proficiency with Python libraries such as pandas, NumPy, scikit-learn, statsmodels, or equivalent tools.
• Understanding of model validation, backtesting, feature engineering, and statistical inference.
• Experience with causal inference methods such as A/B testing, difference-in-differences, or propensity score matching.
• Familiarity with software engineering practices including Git, cloud platforms (Azure/AWS), CI/CD, APIs, SDKs, and model deployment.
• Experience preparing, validating, and integrating large, complex datasets.
• Strong quantitative reasoning, problem-solving, and ability to translate ambiguous business questions into analytical approaches.
• Ability to communicate complex analytical concepts to technical and non-technical audiences.
• Ability to work independently, prioritize effectively, and deliver results in a fast-paced environment.

Benefits

• Excellent benefits package including medical and dental insurance.
• 401(k) plan.
• Paid time off and paid holidays.