We’re committed to putting people first – if you need an accommodation as you go through the job searching, application, and interviewing process, please click here for more information.
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Position requires demonstrable experience in the following, which can be gained in a professional or academic setting: Demonstrating strong analytical skills with solid understanding of statistics and predictive modeling concepts and techniques as applied to insurance operations. Demonstrating an understanding of internal and external data sources, their dependencies, and appropriate uses for each for insurance operations. Demonstrating experience with statistical software packages (R, SAS, Python, and Emblem) to facilitate data gathering and preparation and building of predictive models. Building predictive models, including exploratory data analysis, appropriate data partitioning, selecting appropriate response variable, understanding of appropriateness and tradeoffs of possible model forms, feature engineering, feature selection, model tuning and fit, and model assessment. Working with insurance operations and the procedures of Financial, Underwriting, Claims, Statistical, Information Technology, Legal, and Sales departments. Demonstrating experience with exchanging ideas and conveying complex information clearly and concisely, including verbally, in writing, and via presentation to key stakeholders. Collaborating with partner teams to implement predictive models, including supporting material and responding to Department of Insurance. Domestic travel required up to 10%. 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Design and fine-tune large language/vision models to glean valuable insights from different kinds of unstructured data. Develop and adopt the best-in-class MLOps best practices throughout the data science lifecycle Applies knowledge of sophisticated analytics techniques to manipulate large structured and unstructured data sets in order to generate insights to inform business decisions. Buildout in-house AI/ML products with other team members, including data scientists, IT developers, and business stakeholders Research and develop NLP, CV and other AI/ML models including LLMs, VLMs and GenAI applications to improve risk selection and pricing. Identifies and tests hypotheses, ensuring statistical significance, as part of building and developing predictive models for business application. Translates quantitative analyses and findings into accessible visuals for non-technical audiences, providing a clear view into interpreting the data. Enables the business to make clear trade-offs between and among choices, with a reasonable view into likely outcomes. Customizes analytic solutions to specific client needs. Responsible for smaller components of projects of moderate-to-high complexity. Regularly engages with the data science community and participates in cross-functional working groups. Solid knowledge of predictive analytics techniques and statistical diagnostics of models. Advance knowledge of predictive toolset; expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Has a value-driven perspective with regard to understanding of work context and impact. 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Our data engineers write code that turns trillions of bits of information into structured data--data that our hundred-plus Data Scientists analyze with cutting-edge modeling techniques to unlock insights. From there, our tools and deployment teams ensure this data can be practically applied to business problems across US Retail Markets. Join us and be a part of this dynamic group driving industry-leading data segmentation, fueling the team's success now and into the future. This role may have in-office requirements depending on candidate location The Data Science Infrastructure organization within USRM is hiring a Data Scientist Analyst for its Data Science Tools team. The Data Scientist role will sit within the Data Science Tools team of Infrastructure and be tasked with reengineering the way USRM Data Science approaches the entire modeling pipeline. Their primary goal will be to architect and construct modeling tools and pipelines to massively enhance the ease of iteration, evaluation, and deployment of models and thereby enhance the productivity of the entire USRM Data Science department by orders of magnitude. The objective of this role is to build tools and processes to empower other people. The ideal candidate is proactive, highly technical, and most importantly, driven by a desire to help others and push the boundaries of what we think is possible. Solid knowledge of predictive analytics techniques and statistical diagnostics of models. Advance knowledge of predictive toolset; expert resource for tool development. Demonstrated ability to exchange ideas and convey complex information clearly and concisely. Has a value-driven perspective with regard to understanding of work context and impact. 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Design and develop solutions to support ingestion, curation and provisioning of complex enterprise data to achieve analytics, reporting, and data science Analyze, develop and execute data integration solutions, to manage the information lifecycle needs of an organization. Designs and builds data provisioning workflows/pipelines, physical data schemas, extracts, data transformations, and data integrations and/or designs using ETL and API microservices Builds data architecture and applications that enable reporting, analytics, data science, and data management and improve accessibility, efficiency, governance, processing, and quality of data. Improve speed to market by focusing on current data needs as well as building out long-term strategic data solutions using AWS, Snowflake, SQL, Informatica, as well as other modern data technologies Provide successful deployment and provisioning of data solutions to production or other required environments. Analyze complex technical problems and is expected to recommend process improvements that address complex technology gaps within a single business process and improve data reliability, quality, and efficiency Bachelor's or Master's degree in technical or business discipline or equivalent experience, technical degree preferred. Generally, 5+ years of professional data engineering experience Highly proficient in data engineering languages and tools, and strong proficiency in general programming languages and frameworks; ability to develop on multiple platforms. Required - Experience integrating Guidewire systems creating Data warehousing solutions in Snowflake for reporting, analytics, or data warehousing purposes, often using APIs and ETL tools. Extensive understanding of agile data engineering concepts and processes, such as CICD, pipelines, and iterative development and deployments Demonstrated experience delivering data solutions via agile methodologies on AWS, S3, Athena, Snowflake, IDMC ETL Requires critical thinking, data analysis, and data modeling experience Must be proficient in SQL and experience with Python and JavaScript Experience with ETL (Informatica PowerCenter/IDMC) and knowledge of variety of data platforms and ingestion patterns including eventing & streaming Demonstrate leadership and active pursuit of optimizing data, CI/CD process and tools, test frameworks & practices Must be proactive and self-driven, demonstrate initiative and be a logical thinker. Strong leadership, communication, collaboration skills with a track record of taking solution ownership Thorough knowledge in the following areas: Personal Line P&C insurance, general IT concepts, strategies and methodologies, thorough knowledge of new data architecture principles and concepts, thorough understanding of layered systems architectures, extensive knowledge of business operations, strategies and objectives.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://searchjobs.libertymutualgroup.com/careers/job/618504920181", "isPrivate": false, "latlongs": "43.0717552,-70.7625532"}, {"id": 618505651966, "name": "Associate Data Engineer", "location": "Remote, Remote, United States", "locations": ["Remote, Remote, United States", "Seattle, Washington, United States", "Dallas, Texas, United States", "Boston, Massachusetts, United States"], "hot": 0, "department": "Technology", "business_unit": "0027-LM Technology-US", "t_update": 1753990500, "t_create": 1753114680, "ats_job_id": "71349", "display_job_id": "2025-71349", "type": "ATS", "id_locale": "2025-71349-en", "job_description": "Assists with collecting, integrating, and analyzing organizational data with the purpose of drawing conclusions from that information. 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Performs other projects and duties as assigned Use advanced technical skills (e.g., Python, SQL, other relevant tools) to automate, maintain, and enhance complex data processes that support GRS reserving. You will be responsible for handling large datasets used in the reserving process and the related analytics. 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Collaborate with diverse teams of data engineers and product partners who believe in continuous learning, respectful collaboration, and inclusive data solutions. Build and optimize scalable, reliable, high-performance automated data pipelines and solutions using Snowflake and AWS. Drive data quality, governance, and observability practices throughout the development lifecycle. Stay current with industry trends and best practices in data engineering and recommend improvements to existing processes. Contribute to a culture of learning by mentoring junior engineers, participating in code reviews, and promoting engineering excellence and best practices. A bachelor's degree in technical or business discipline, or equivalent experience. 5+ years of experience in data engineering, with designing, building, and optimizing scalable data pipelines and architecture. Expert-level proficiency in SQL and a deep understanding of data modeling, data warehousing, and data architecture best practices. Intermediate experience using Python for data transformation, integration, and automation. Hands-on experience with modern public cloud-based data platforms--Snowflake, AWS (e.g., S3, Glue, Lambda). Proficiency with ETL/ELT pipelines and patterns for loading data warehouses and data lakes. Hands-on experience building and deploying CI/CD pipelines (e.g., GitHub Actions, Bamboo, or similar tools). Exposure to the Investments or Finance domain is highly desirable. Familiarity with building and consuming APIs (e.g., REST, API Gateway). Awareness of security best practices, including access control, authentication, and secure processing.", "stars": 0.0, "medallionProgram": null, "location_flexibility": null, "work_location_option": "onsite", "canonicalPositionUrl": "https://searchjobs.libertymutualgroup.com/careers/job/618505375104", "isPrivate": false, "latlongs": "42.3555076,-71.0565364"}, {"id": 618503322305, "name": "Senior Cloud Data Engineer, Investments Technology", "location": "Boston, Massachusetts, United States", "locations": ["Boston, Massachusetts, United States"], "hot": 0, "department": "Technology", "business_unit": "0027-LM Technology-US", "t_update": 1749497400, "t_create": 1743432660, "ats_job_id": "69494", "display_job_id": "2025-69494", "type": "ATS", "id_locale": "2025-69494-en", "job_description": "Does the thought of building a modern cloud-based data platform from the ground up excite you? If yes, then join us and be part of a team that's forging the path to data-driven success. Note: This role has a hybrid work arrangement in our Boston, MA office (2x/week) We have a large and varied customer base, supported by strategic business units that function as a complete investment firm within our Fortune 100 Company. Our investment professionals are critical to our ability to keep our promises to policyholders, claimants, and their families. We are an equal opportunity employer and value diversity at our company. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. A bachelor's degree in technical or business discipline, or equivalent experience 5+ years of experience in data engineering, with a proven track record of designing, building, and optimizing scalable data pipelines and architectures Expert-level proficiency in SQL and strong experience with Python for data transformation, automation, and orchestration Deep understanding of data modeling, data warehousing, and data architecture best practices Hands-on Experience with modern public cloud-based data platforms - Snowflake (preferable), AWS, Azure Advanced proficiency in AWS services including (but not limited to): S3, Glue, Lambda, EC2, and Athena Proficient with ETL/ELT data pipelines, patterns for loading Data Warehouses, Lakes Solid experience with DevOps practices and automation frameworks, including CI/CD pipelines, GitHub Actions, Bamboo, and pipeline-as-code principles Skilled in BI/reporting tools (e.g., Power BI, Tableau) Previous experience in Investments / Asset Management, Finance Data modeling, is highly desirable. Familiarity in building and consuming APIs (REST, API Gateway, etc.) Cognizance of security concerns, from access control and authentication to secured processes. Knowledge of containerization and orchestration tools (e.g.: Docker, Kubernetes) A comprehensive understanding of agile environments and the ability to adapt to rapidly changing circumstances. Work in Data and Analytics squad to design, develop, and enhance the data and analytics solutions and pipelines, driving data-driven decision-making processes. Collaborate with scrum masters, product owners, and engineering teams to iteratively create data and analytics solutions that meet both business and technical requirements. Work closely with development team in building Innovative, cloud native solutions using AWS, Snowflake, Python, Rest API etc. Continuously enhance full delivery pipeline through automation, expanded yet increasingly efficient test coverage, ultimately optimizing time-to-market and overall quality Optimize and fine-tune existing data pipelines for performance, scalability, and reliability. Implement data quality checks and monitoring processes to ensure data accuracy and consistency. Stay current with industry trends and best practices in data engineering and recommend improvements to existing processes. Mentor junior engineers in the team; collaborate in technical documentation; participate in code reviews and adhering to engineering excellence/best practices. 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Analytics & Visualization Build and refine analytic models, develop robust data pipelines and interactive dashboards, leveraging advanced Python, SQL, Dash and Power BI to translate complex data sets into actionable insights. Machine Learning & AI Employ statistical techniques, machine learning algorithms, and GPT-based solutions to improve market analysis, develop investment strategies, and enhance decision-making processes. Innovation & Continuous Improvement Proactively identify opportunities to upgrade capabilities and stay at the forefront of fixed-income analytics. Monitor industry trends, market developments, and new analytical tools to maintain a competitive edge. Numerate self-starter with a bachelor's degree in finance, Economics, Mathematics, Computer Science, Engineering, or a related field (Master's degree a plus). 3+ years of experience in portfolio analysis or investment management, with a strong preference for quantitative fixed-income experience. 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