Overview Cotiviti is a leading solutions and analytics company that leverages financial datasets to deliver deep insight into the performance of retail system. These insights uncover new opportunities for retail organizations to collaborate to improve their financial performance and accuracy along with reducing inefficiencies. Cotiviti is seeking a hands‑on Data Scientist to build production‑ready machine learning solutions that power the next generation of Retail Technology. You’ll work with large on premise and cloud datasets, apply AI/ML to real-world identification and validation challenges, and collaborate directly with auditors and engineers to deliver high‑impact results. This role is perfect for an ambitious technologist who wants to ship real models—not conduct research—and make a direct impact in a fast‑moving environment. This role aligns with our Retail Recovery Audit solution. Cotiviti Retail. This role can be located within the US or Canada (Ontario area only) Responsibilities - Work with key stakeholders both within R&D and Operations, along with product management to assess the potential value and risks associated with business problems that have the potential to be solved using machine learning and AI techniques. - Develop an exploratory data analysis approach to verify the initial hypothesis associated with potential AI/ML use cases. - Document your approach, thinking and results in standard approaches to allow other data scientists to collaborate with you on this work. - Prepare your final trained model and develop a validation test set for QA. - Work with production operations to deploy your model into production and support them in monitoring model performance. - Participate in other data science teams collaborating with your peers to support their projects - Participate in knowledge sharing sessions to bring new insights and technologies to the team. - Participate in design sessions to continuously develop and improve the Cotiviti machine learning platform - Provide End to End value-based solutions, including data pipeline, model creation and application for end user consumption. - Complete all responsibilities as outlined in the annual performance review and/or goal setting. - Complete all special projects and other duties as assigned. - Must be able to perform duties with or without reasonable accommodation. This job description is intended to describe the general nature and level of work being performed and is not to be construed as an exhaustive list of responsibilities, duties and skills required. This job description does not constitute an employment agreement and is subject to change as the needs of Cotiviti and requirements of the job change. Qualifications - MS or PhD. Degree in relevant discipline (Math, Statistics, Computer Science, Engineering or Health Sciences) or commensurate professional work experience. - 4-7 years’ experience in advanced analytics - 3+ years’ experience in working in Big Data environments - Significant experience with SQL, ability to write SQL queries to efficiently extract data from relational databases - Experience developing machine learning models in an exploratory data analytics environment and working with others to develop production ready versions of the models that are deployed within operational environments - Experience in using machine learning tools to develop production strength models including, but not limited to, Python, TensorFlow, pandas, numpy, scikit-learn, spark, scala, hive, impala - Applied Machine Learning: Application of a variety of machine learning techniques to increase identification of payment integrity issues for our clients, reduce the cost of auditing processes or increase the quality of care and outcomes. Must have implemented machine learning solutions within production environments at scale - Big Data Analysis: Strong ability to manage and analyze data in a Big Data environment using a variety of scripts, potentially including but not limited to Scala/Spark and Python as well as Cloud based ML/AI capabilities. Mental Requirements: - Critical Thinking: Ability to think critically and evaluate information objectively, considering different perspectives and potential implications before drawing conclusions or making recommendations. - Attention to Detail: must have a keen eye for detail to ensure accuracy in data analysis, interpretation, and reporting. - Quantitative Aptitude: Strong numerical skills are essential for conducting quantitative analysis, working with statistical methods and models, and manipulating data using mathematical operations. - Data Interpretation: skilled in interpreting data visualizations, charts, graphs, and other forms of data presentation to extract meaningful insights and communicate findings effectively. - Communication Skills: Effective communication skills are crucial for conveying complex technical concepts and insights to non-technical stakeholders clearly and understandably through written reports, presentations, and verbal discussions. - Curiosity and Learning Agility: A strong desire to learn and explore new methodologies, techniques, and tools in the field of data analysis and insights generation is essential for staying current with industry trends and best practices. - Resilience: The ability to handle pressure, adapt to changing priorities, and overcome setbacks is important in a fast-paced and sometimes ambiguous analytical environment. - Ethical and Integrity: Upholding ethical standards and maintaining integrity in handling sensitive data and information is paramount for building trust and credibility in the insights provided Physical Requirements and Working Conditions: - Remaining in a stationary position, often standing or sitting for prolonged periods. - Repeating motions that may include the wrists, hands, and/or fingers. - Must be able to provide a dedicated, secure work area. - be able to provide high-speed internet access / connectivity and office setup and maintenance. - No adverse environmental conditions expected. Base compensation ranges from $110,000 to $140,000 per year. Specific offers are determined by various factors, such as experience, education, skills, certifications, and other business needs. Cotiviti offers team members a competitive benefits package to address a wide range of personal and family needs, including medical, dental, vision, disability, and life insurance coverage, 401(k) savings plans, paid family leave, 9 paid holidays per year, and 17-27 days of Paid Time Off (PTO) per year, depending on specific level and length of service with Cotiviti. For information about our benefits package, please refer to our Careers page. This role is based remotely and all interviews will be conducted virtually. Date of posting: 03/16/2026 Applications are assessed on a rolling basis. We anticipate that the application window will close on 06/15/2026, but the application window may change depending on the volume of applications received or close immediately if a qualified candidate is selected. #LI-REMOTE #LI-RA1

Overview Join an amazing team that is consistently recognized for our achievements and culture, including our most recent Forbes award of being one of America's Best Midsize Employers for 2026! Position Summary: The Principal Mitigation Scientist is responsible for driving Mercury’s evolving understanding of how patterns of home hardening and community scale fuel treatment influence wildfire entry, spread, and loss outcomes at the community level. The successful candidate will serve as the founding architect for the Mercury Mitigation Model working closely with the Sr. Director of Climate and Catastrophe Science and partners across Climate and Catastrophe Science, Underwriting, R&D, Pricing, Product, and Reinsurance. This role requires deep expertise in community scale wildfire and WUI/urban fire behavior – including wind, topography, wildland and urban fuels, and the design and evaluation of fuel treatment (natural and urban fuels). The successful candidate will demonstrate expertise in home hardening programs – and hands on experience with operational wildfire models that combine vegetation spread, structure focused high-resolution urban fire modules, probabilistic ember generation and cellular automaton spread, and drought/wind frameworks along with downslope/Santa Ana indicators and fuel moisture indices. Experience with fire-pathway analysis is required to quantify how specific mitigation patterns change loss outcomes. Working as part of a multidisciplinary team, the successful candidate will translate mitigation science into strategy that shows up in Mercury’s models, rate structures, underwriting rules, reinsurance analyses, and community commitments. Responsibilities Essential Job Functions: - Lead the design and development of the first generation Mercury Mitigation Modeling framework for California Wildfire, including the architecture that links hazard and loss outputs to mitigation configurations and decision ready business outputs. - Define a clear, feasible roadmap for wildfire mitigation modeling that technical teams and business stakeholders believe is implementable. - Lead and independently contribute to community scale fire pathways analysis to identify points of fire entry, key parcels and corridors, and reusable mitigation patterns. - Partner with catastrophe and hazards scientists to translate their research into inputs for community spread frameworks, parcel and community level mitigation scores, and scenario design. - Partner with hazards and catastrophe scientists to lead risk assessments using both third-party platforms (e.g., Verisk-AIR, Moody’s-RMS, KCC, etc.) and by developing proprietary models to improve loss assumptions and wildfire hazard views. - Lead and independently contribute to the development of scientific datasets, event catalogs, and physical hazard frameworks tailored to insurance and reinsurance applications. - Contribute to the analysis of extreme weather and climate datasets to produce climate-conditioned views of risk and forward-looking hazard projections. - Contribute to technical validation of vendor risk scores and support internal benchmarking efforts using policy-data, claims, and other proprietary datasets. - Lead cross-functional collaboration with Actuarial, Product, Underwriting, and R&D teams to ensure scientific rigor in pricing and capital allocation decisions. - Drive internal education, white papers, and occasional public-facing insights to strengthen Mercury’s position as a leader in climate and catastrophe science. - Lead and independently contribute to original research and synthesize findings from the broader scientific community. - Lead and develop reports, presentations, and data visualizations to effectively communicate findings to technical and non-technical teammates. - Lead and develop real-time risk analytics and detailed loss estimates to assess event impact. - Remain at the forefront of scientific advancements in event simulation, hazard characterization, and high-resolution modeling of natural perils. - Represent Mercury in external research collaborations, industry working groups, and technical forums. Qualifications Education: Minimum: - A combination of education and experience is required with an emphasis in Wildland Fire Science, Fire Protection Engineering, Forest or Ecosystem Science, Atmospheric Science, Civil/Environmental Engineering, Applied Physics, Applied Mathematics, Computer Science, or a closely related field Preferred: - Ph.D. with an emphasis in high resolution fire behavior modeling Experience: Minimum: - 10-Years or equivalent combination of education and experience. Advanced degrees may substitute for a portion of professional experience (graduate education typically counts toward 2–5 years of experience) Preferred: - 15-Years + Ph.D Knowledge and Skills: Minimum: - Expertise applying communityscale wildfire and WUI/urban fire behavior, with direct application to fire entry, spread pathways, ember exposure, and loss outcomes at parcel and neighborhood scales. - Expertise designing, evaluating, or operating fueltreatment and homehardening programs (e.g., defensible space, fuel breaks, canopy management, roof/vent/deck/siding upgrades) and quantifying their impact on loss. - Demonstrated proficiency applying operational wildfire models that combine Rothermelclass vegetation spread, structurefocused (Hamadastyle) urban fire modules on ~100 m grids, probabilistic ember generation and cellularautomaton spread, and wind/drought frameworks (e.g., NARR/HRRRclass winds, downslope/Santa Ana/Diablo indicators, fuelmoisture indices). - Expertise applying advanced fire and pathwaymodeling tools such as Technosylvaclass simulators, WRFFire/WRFSFIRE, FlamMap/FARSITEstyle tools, Xyloplan, graphbased spread frameworks, AGNINARstyle approaches, or closely related systems. - Experience within an insurance, reinsurance, catastrophe modeling, utility, or publicsector mitigation environment where wildfire/WUI analytics are tied to real operational or financial decisions. - Proven ability to translate complex mitigation and firebehavior science into actionable frameworks that can be applied to pricing, underwriting, reinsurance, and community commitments (e.g., mitigation credits, eligibility rules, tailrisk narratives, communitytiering). - Clear, concise communication skills with a track record of collaborating across multiple disciplines such as scientists, engineers, actuaries, product managers, and underwriters, and explaining uncertainty and tradeoffs to nontechnical stakeholders. - Demonstrated proactive mindset with experience in strategic planning and a strong sense of ownership; capable of driving research and directing multiple project streams with competing priorities and delivering high-quality outputs independently. - Proven to be a highly motivated, critical thinker with strong problemsolving skills and a proactive, ownershiporiented mindset; capable of strategic planning and independently driving multiple, competing project streams to highquality delivery. - Demonstrated passion for building mitigationforward capabilities including crosssector collaboration with insurers, academia, vendors, utilities, labs, and public agencies. - Evidenced commitment to ongoing learning on the latest wildfire/WUI research, mitigation science, modeling techniques, and regulatory/market trends. - Strong collaboration orientation: able to lead partnerships with Actuarial, Product, Underwriting, CAT Pricing, R&D, and technology teams to ensure mitigation modeling directly informs business decisions. Preferred: - Experience working with largescale weather, climate, and hazard datasets relevant to wildfire and WUI (e.g., ERA5/NARR/HRRR, LANDFIRE, MTBS, GRIDMET, MODIS/VIIRS, FPAFOD, NCEI/Storm Events, local fuels and building datasets) to analyze physical drivers of ignition, spread, and urban conflagration. - Experience with catastrophe modeling platforms (e.g., Verisk-AIR, Moody’s-RMS, KCC, etc), with ability to interpret and critique hazard and vulnerability assumptions. - Experience developing statistical and scenario based modeling applied to natural hazards or mitigation (e.g., vulnerability/EP modeling, scenario catalogs, treatment vs. no treatment comparisons, before/after loss deltas). - Programming and data analysis skills using Python and/or R, including experience with geospatial libraries and data pipelines (e.g., GeoPandas, xarray, rasterio, shapely, GDAL, PostGIS). - Experience with catastrophe modeling platforms (e.g., Verisk/AIR, Moody’s RMS, KCC) and the ability to interpret, critique, and augment their wildfire hazard, vulnerability, secondary modifiers, and urban conflagration assumptions. - Experience developing or refining wildfire hazard footprints, stochastic event sets, or scenario based mitigation views (e.g., before/after catalogs for specific communities) for insurance or utility applications. - Quantitative skills and experience calculating AAL, EP curves, VaR/TVaR, tail metrics, and mitigation deltas (e.g., change in AAL/tail for specific treatment bundles or home hardening configurations). - Experience with modern data and modeling tooling (e.g., SQL, NumPy, Pandas, SciPy, Dask, PySpark, xarray, cloud data platforms) for handling large geospatial and event datasets. - Experience with advanced modeling techniques including artificial intelligence as applied to extreme weather datasets. - Experience with downscaling techniques in dynamical weather models. - Direct experience in catastrophe risk management (rating, underwriting, or reinsurance) where wildfire/WUI mitigation analytics materially influenced decisions. About the Company Why choose a career at Mercury? At Mercury, we have been guided by our purpose to help people reduce risk and overcome unexpected events for more than 60 years. We are one team with a common goal to help others. Everyone needs insurance and we can’t imagine a world without it. Our team will encourage you to grow, make time to have fun, and work together to make great things happen. We embrace the strengths and values of each team member. We believe in having diverse perspectives where everyone is included, to serve customers from all walks of life. We care about our people, and we mean it. We reward our talented professionals with a competitive salary, bonus potential, and a variety of benefits to help our team members reach their health, retirement, and professional goals. Learn more about us here: https://www.mercuryinsurance.com/about/careers #LI-ZK1 Perks and Benefits We offer many great benefits, including: - Competitive compensation - Flexibility to work from anywhere in the United States for most positions - Paid time off (vacation time, sick time, 9 paid Company holidays, volunteer hours) - Incentive bonus programs (potential for holiday bonus, referral bonus, and performance-based bonus) - Medical, dental, vision, life, and pet insurance - 401 (k) retirement savings plan with company match - Engaging work environment - Promotional opportunities - Education assistance - Professional and personal development opportunities - Company recognition program - Health and wellbeing resources, including free mental wellbeing therapy/coaching sessions, child and eldercare resources, and more Mercury Insurance is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by federal, state, or local law. Pay Range USD $128,136.00 - USD $252,194.00 /Yr.

• Design models and systems that help automate and optimize claims handling, including; Liability prediction, Fraud detection, Document & image understanding. • Partner with product and engineering teams to translate claims workflows into ML solutions • Prototype production-ready models and data pipelines • Dive deep on early stage products to scale product market fit • Help shape the data science roadmap and architecture across Assured’s platform

Role Description Redhawk Administrative Services, LLC is currently seeking a Data Analytics Modeler. The Air Force Accounting and Policy Operations (SAF/FMFO) Data and Analytics Team is responsible for working with the Defense Enterprise Accounting Management System (DEAMS) Integrated Process Team (IPT) Leads and Legacy system subject matter experts (SMEs) to help ensure the successful rollout of financial dashboards to end users as well as the sustainment of the Oracle ERP technology and financials modules for DEAMS. The Data and Analytics Team provides functional and configuration knowledge and manages client relationships. Potential candidates must have experience in Oracle Federal Financials, Department of Defense Financial Management, and full life-cycle implementation. The successful candidate will interact with the functional teams (Procure-to-Pay, Orders-to-Cash, Budget-to-Report, Projects, or Customer Support) and end users in support of the creation and sustainment of financial dashboards. The candidate will provide business process reengineering (BPR) assistance and help the teams through agreed upon phases of project governance, problem solving, and training. Qualifications - Bachelor's Degree in Computer Science, Statistics, Mathematics, or related field - 5 years of experience in BI/ Data Analytics - Proficient with Microsoft Power Platform (PowerBI, PowerAutomate, Power Apps, etc.) - Secret Security clearance required or higher - Must be willing and able to travel as required Requirements - Developing Dashboards and User Facing Products in Microsoft Power Platform - Developing strategies to identify areas of opportunity to optimize statistical efficiency and quality of accounting data - Performing financial data collection through available tools (Discoverer, OBIEE, and APEX) - Analyzing/Interpreting data and recommending corrective (preventative) actions - Identifying accounting anomalies and recommend corrective (preventative) actions - Monitoring unposted transactions and recommend corrective (preventative) actions - Documenting Standard Operating Procedures (SOP) to correct identified anomalies - Performing/Processing actions to correct identified anomalies - Researching trouble tickets to determine course of action - explain, resolve, coordinate and transfer to the appropriate team, or send to triage for system integrator (SI) resolution - Ability to understand issue and analyze root cause through direct customer interaction; brings expertise in Oracle based solution to resolve complex issues and cross functional issues; and communicates with clients effectively in order to resolve customer issues - Ability to leverage Oracle Federal Financials functionality and domain knowledge to assist in solution development Company Description Redhawk Administrative Services, LLC is an equal opportunity employer. Redhawk Administrative Services, LLC does not discriminate in employment opportunities or practices on the basis of race, color, religion, sex, national origin, age, disability, marital status or any other characteristic protected by law.