Data Scientist

Data Scientist Remote Opportunity:

At Morgan & Morgan, the work we do matters. For millions of Americans, we’re their last line of defense against insurance companies, large corporations or defective goods. From attorneys in all 50 states, to client support staff, creative marketing to operations teams, every member of our firm has a key role to play in the winning fight for consumer rights. Our over 6,000 employees are all united by one mission: For the People.

• →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.

• 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.

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