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The Role
Lead end-to-end predictive analytics initiatives, translating business objectives into data science solutions. Develop and deploy machine learning, deep learning, statistical modeling, and computer vision models for insurance use cases. Manage experimentation, model validation, monitoring, reproducible workflows, cloud deployment, production pipelines, and incident troubleshooting. Partner with stakeholders, communicate findings to senior audiences, document methodologies, and provide technical guidance through code reviews and best practices.
Summary Generated by Built In
Description
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
Qualifications
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
About Us
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
$193,232.00 - 225,000.00
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
Qualifications
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
About Us
Duties:
Lead end-to-end delivery of predictive analytics solutions, including problem definition, data preparation, feature engineering, model development, validation, and deployment.
Independently translate business objectives into analytic approaches, success metrics, and project plans for medium-sized data science initiatives.
Partner with cross-functional stakeholders to gather requirements, prioritize use cases, and align analytics deliverables with business strategy.
Apply machine learning, deep learning, statistical modeling, and optimization techniques to large structured and unstructured datasets to generate insights and predict outcomes.
Identify and test hypotheses using appropriate statistical methods and evaluate model performance to ensure statistical rigor of findings.
Develop and apply computer vision methods for insurance-domain use cases, including model training, evaluation, and monitoring.
Produce clear visualizations and written/presented narratives that communicate findings and recommendations to non-technical and senior audiences.
Document modeling methodology, assumptions, limitations, and results to support reproducibility and governance and implement monitoring to maintain model performance over time.
Provide technical guidance through code review and best-practice recommendations to support team execution and consistency.
Position requires domestic travel up to 10%.
Telecommuting permitted up to 60%.
Requirements:
Employer will accept a PhD degree in Computer Science, Statistics, or related field and two (2) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Alternatively, employer will accept a Master's degree in Computer Science, Statistics, or related field and four (4) years of experience in the job offered or in an Asst Dir, Data Science (STP)-related occupation.
Position requires demonstrable experience in the following:
Conduct experiments and data-driven studies to support business decisions in an insurance setting, including hypothesis testing, interpreting results, and communicating findings.
Deliver strategic decision support using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools.
Implement data and model artifact versioning and reproducible ML workflows using version control tools, including DVC and MLflow.
Build and maintain monitoring dashboards and production data pipelines using data warehouse and workflow orchestration tools, including Snowflake and Airflow.
Apply geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms.
Train, evaluate, and improve computer vision models, including object detection and segmentation, and perform model performance diagnostics and error analysis.
Optimize deep learning training performance through profiling, efficient data loading, and training optimizations.
Develop, deploy, and operate data science solutions using public cloud platforms, including AWS and Azure.
Containerize ML services and pipelines using Docker and apply CI/CD and deployment automation practices for production releases.
Monitor and troubleshoot production scoring and data pipelines, including performance tracking, incident triage, and root-cause analysis.
Develop and maintain labeling guidelines and label taxonomies for computer vision datasets, and coordinate with annotation resources to support dataset development.
Experience may be gained during graduate program. Will accept any suitable combination of education, training, and/or experience. Multiple Positions Available.
$193,232.00 - 225,000.00
Skills Required
- PhD in Computer Science, Statistics, or a related field plus two years of experience in the job offered or a related occupation
- Alternatively, a Master's degree in Computer Science, Statistics, or a related field plus four years of experience in the job offered or a related occupation
- Experience conducting experiments and data-driven studies in an insurance setting, including hypothesis testing, interpretation, and communication of findings
- Experience using insurance analytics and pricing platforms, including Emblem, Earnix, or equivalent tools
- Experience implementing artifact versioning and reproducible machine learning workflows using DVC and MLflow
- Experience building monitoring dashboards and production data pipelines using Snowflake and Airflow
- Experience applying geospatial fundamentals, including coordinate reference systems, map projections, and pixel-to-geo transforms
- Experience training, evaluating, and improving computer vision models for object detection and segmentation
- Experience optimizing deep learning training performance through profiling, efficient data loading, and training optimizations
- Experience developing, deploying, and operating data science solutions using AWS and Azure
- Experience containerizing machine learning services and pipelines with Docker and applying CI/CD deployment automation
- Experience monitoring and troubleshooting production scoring and data pipelines, including incident triage and root-cause analysis
- Experience developing computer vision labeling guidelines and taxonomies and coordinating annotation resources
What the Team is Saying
Natalie
Product Owner II
Antonio
Software Engineer, Global Finance Technology
Tyler
Principal Software Engineer
Sandra
Product Owner I
Kemi
Associate Software Engineer
Herman
Associate Software Engineer
Chase
Software Engineer
Ethan
Manager, Engineering
Sandra
Product Owner I
Liberty Mutual Insurance Compensation & Benefits Highlights
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Parental & Family Support — Paid bonding leave covers 8 weeks for all parents, with birth parents receiving up to 16 weeks at 100% pay, alongside substantial family‑building support such as up to $40,000 for adoption/surrogacy and fertility coverage up to $50,000 medical/$25,000 pharmacy.
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Retirement Support — The 401(k) structure includes a 100% match up to 5% each pay period plus an additional 3% year‑end contribution for eligible employees, and student‑loan payments can be matched in the same way as 401(k) contributions.
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Healthcare Strength — Comprehensive medical, dental, and vision offerings are paired with robust mental‑health resources, including an Employee Assistance Program and 12 free therapy sessions for employees and family members, with on‑site health centers in Boston and Plano.
Liberty Mutual Insurance Insights
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Liberty Mutual Insurance Culture & Values
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The Company
What We Do
Liberty Mutual Insurance exists to help people embrace today and confidently pursue tomorrow. A Fortune 100 company and global leader in property and casualty insurance, we’ve spent over a century creating innovative products, services and technologies to meet the world’s ever-changing needs and make a difference for our customers and communities.
Why Work With Us
We offer the agility, work flexibility, project ownership and access to emerging tech you’d expect from a start-up — combined with the stability, resources and benefits that come from working at a leading Fortune 100 company. All in a welcoming, inclusive environment that values the unique insights, perspectives and backgrounds of each person.
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Liberty Mutual Insurance Teams
Digital & Technology
About our Teams
Liberty Mutual Insurance Offices
Hybrid Workspace
Employees engage in a combination of remote and on-site work.
Typical time on-site:
Flexible
