Role Description As a Senior Data Scientist at Coalition you will be responsible for measuring, understanding, and helping optimize Coalition’s underwriting. You will perform statistical analysis to provide data-driven insights. You will help us understand and improve our cyber risk selection and reduction, pricing and automation in order to grow our revenue in a safe and efficient manner. - Analyze diverse datasets including claims data, cybersecurity risk signals, and underwriting databases to extract meaningful patterns and insights. - Large scale data analysis with the objective of producing valuable risk signals to be used for underwriting or risk evaluation of organizations. - Develop and refine statistical and machine learning models to assess cybersecurity risks with applications to underwriting and pricing. - Create comprehensive reports on underwriting efficiency metrics and risk selection quality to inform strategic decisions. - Apply statistical techniques to evaluate and improve our cyber risk assessment methodology. - Provide analytical support for underwriting strategies to balance growth with risk management. - Identify, analyze, and implement opportunities to automate underwriting workflows using advanced analytics and data-driven strategies. - Collaborate cross-functionally with actuarial, product, and engineering teams to implement data-driven improvements. Lead cross-functional initiatives in area of expertise. - Serve as both a technical lead and a mentor within the underwriting team. - Work alongside other data scientists, software engineers, and cyber security engineers to improve underwriting at Coalition. Intellectual curiosity and proactive approach to identifying improvement opportunities. Qualifications - Bachelor's degree in a quantitative field (e.g., Statistics, Mathematics, Computer Science, Actuarial Science, or related discipline). - 5+ years of experience in underwriting, quantitative analysis, or risk modeling in the insurance industry. - Knowledge of underwriting, pricing, reserving, or risk management processes in insurance. - Advanced SQL skills for querying complex databases and joining disparate data sources. - Expertise in data manipulation and analysis using Python, R, or similar analytical tools. - Experience with data visualization tools (e.g., Tableau, Power BI, Looker). - Experience with dashboard creation for operational metrics tracking. - Strong communication and analytical skills. - Capable of working with remote teams. Requirements - Actuarial qualification (nice-to-have). - Experience in cyber insurance or cyber security (nice-to-have). - Prior experience or willingness to learn how to work with large datasets and big data technologies (Snowflake, AWS Athena) (nice-to-have). Benefits - 100% medical, dental, and vision coverage. - Flexible PTO. - Annual home office stipend and WeWork access. - Mental & physical health wellness programs like Headspace, Lumino, and more! - Competitive compensation and opportunity for advancement.

Role Description Join us in building the intelligence that powers product discovery for millions of shoppers and thousands of merchants across the Middle East. As a Senior Data Scientist for the Recommendation Systems Pod, you'll lead the design and execution of large-scale personalization models that directly impact the company topline. This is a rare opportunity to shape the next generation of commerce AI in a high-growth market characterized by highly diverse user and merchant behaviors across the GCC. - Design, train, and deploy recommendations/personalization models leveraging deep learning, sequence models (Transformers, GRU), and boosted trees (XGBoost, LightGBM). - Develop multi-objective ranking that blends engagement, conversion, and merchant value into a single ranking score (value model), using multi-task learning where shared representations help. - Build scalable two-stage retrieval and ranking systems — ANN retrieval (FAISS, ScaNN) over user/product/event embeddings feeding learning-to-rank models (pointwise, pairwise, and listwise objectives). - Collaborate with infra to productionize real-time feature pipelines (ClickHouse, Kafka, Spark). - Define serving-time impression and feature logging to eliminate training-serving skew and produce unbiased training data. - Design and run online experiments with rigorous guardrail metrics; correct for position and presentation bias in logged data; apply counterfactual/off-policy evaluation and uplift modeling to attribute lift accurately. - Integrate model outputs with platform APIs for dynamic personalization in search, home feeds, and store pages. - Define best practices for offline evaluation (MAP@K, NDCG) and online experimentation metrics (CTR, CVR, GMV uplift). - Partner with product analytics and data science to iterate on signal enrichment and cold-start strategies. - Mentor junior data scientists and define best practices. Qualifications - Bachelor's or Master's degree in Computer Science, Machine Learning, or a related technical field. - 4+ years of hands-on ML experience, including 2+ years designing or deploying large-scale recommendation systems. - Track record: Built or maintained systems serving 1M+ users or generating 100M+ personalized predictions daily. - Deep expertise in representation learning, embeddings, attention mechanisms, and multi-task learning. - Demonstrated success integrating multi-stage ranking systems across e-commerce surfaces (search, feeds, product detail pages) with measurable online lift (CVR, GMV). - Proficient with large-scale data ecosystems: Kafka, Spark, ClickHouse, BigQuery, or equivalent. - Strong command of experimentation rigor: guardrail metrics, position-bias correction, off-policy/counterfactual evaluation, and model monitoring. - Skilled in debugging, optimization, and productionization of ML pipelines in cloud or containerized environments.

• Develop analyses to identify patterns, trends, and correlations among indicators • Implement mechanisms for automated generation of insights for governance • Explore the use of AI / LLM models for interpreting and explaining data • Develop a conversational assistant solution for querying and analyzing information • Support the evolution of the governance model toward a data-driven approach

Title: High Explosive Modeler - Staff Scientist Location: Livermore United States Employees work in a hybrid modemay offer a hybrid schedule Full-time Organization: Physical and Life Sciences Category: Physical Life Sciences Job Code 2: SES.4 Science & Engineering MTS 4 Employee Referral Bonus: Not applicable Job Code 1: SES.3 Science & Engineering MTS 3 Pre-Employment Drug Test: Required for external applicant(s) selected for this position (includes testing for use of marijuana) Pre-Placement Medical Exam: Not applicable Security Clearance: Anticipated DOE Q clearance (requires U.S. citizenship and a federal background investigation) Position Type: Flexible Term Job Description: Join us and make YOUR mark on the World! Lawrence Livermore National Laboratory (LLNL) has turned bold ideas into world-changing impact advancing science and technology to strengthen U.S. security and promote global stability. Our mission spans four critical national security areas nuclear deterrence, threat preparedness, energy security, and multi-domain defense empowering teams to take on the toughest challenges of today and tomorrow. With a culture built on innovation and operational excellence, LLNL is a place where your expertise can make a real impact. We have an opening for a Staff Scientist to perform research, development, and application activities in support of Laboratory missions. You will be an integral member of an interdisciplinary team responsible for conducting and supporting research in the development and application of high explosive models predicting detonation performance in response to complex stimuli. This position is in the Reaction Dynamics Group within the Energetic Materials & Design (EMD) section of the Materials Science Division supporting the Energetic Materials Center (EMC). This position may offer a hybrid schedule, which includes the flexibility to work from home one or more days per week, after a probationary period. The specifics of the hybrid schedule, including the exact number of days required in the office and virtual work options may vary based on the needs of the team and the organization. This position will be filled at either level based on knowledge and related experience as assessed by the hiring team. Additional job responsibilities (outlined below) will be assigned if hired at the higher level. You will - Develop advanced high explosive detonation models based on fundamental chemical and physical mechanisms. - Interact with experimental groups and incorporate information from advanced experiments into current and developmental high explosives models to improve model fidelity. - Systematically validate models against a suite of experimental data. - Develop uncertainty quantification methods based on utilizing statistical approaches and/or machine learning. - Provide solutions and creative approaches to ensure project needs are addressed in a timely and efficient manner. - Contribute to project/program milestone completion and provide solutions to complex problems that may not be well defined. You will be expected to interact closely with other team members as well as work independently with minimal direction when appropriate. - Interact with the physics and engineering design staff and other users to support responsive model development and insertion options. - Perform other duties as assigned. Additional job responsibilities, at the SES.4 level - Contribute to developing a strategy to improve detonation models by incorporating initial conditions, morphology, and chemical variations to expand capabilities into adjacent domains. - Actively support and guide experimental campaigns involving energetic materials to benchmark current models and advance model development to address complex problems relevant to national security missions. - Create innovative methods, technologies, and knowledge in detonation modeling. - Ability to secure and maintain a U.S. DOE Q-level security clearance which requires U.S. Citizenship. - PhD. in chemistry, physics, or the equivalent combination of education and related experience. - Knowledge of shock wave physics, physical and chemical kinetics, statistical and thermodynamic methods. - Familiarity with scientific programming, code development practices, and proficiency in one or more high-level programming/scripting languages. - Advanced written and verbal communication skills necessary to deliver presentations and prepare written reports, explain technical information. - Advanced interpersonal skills necessary to interact with a diverse set of scientists, engineers, and other technical and administrative staff. Additional qualifications at the SES.4 level - Demonstrated expertise in detonation physics and energetic materials modeling. - Demonstrated ability to lead complex technical projects and a strong track record mentoring scientific staff. - Strong track record of publications and/or technical reporting. Qualifications We Desire - Experience working in energetic materials research at a Department of Energy (DOE) and/or Department of Defense (DoD) facility or equivalent with stringent safety and security policies. - Knowledge of supercomputer applications in material modeling and energetic materials research. - Knowledge of machine learning techniques applicable to material model development and/or complex hydrodynamic simulations. Pay Range $175,530 - $267,060 Annually $175,530 - $222,564 Annually for the SES.3 job level $210,630 - $267,060 Annually for the SES.4 job level This is the lowest to highest salary we in good faith believe we would pay for this role at the time of this posting; pay will not be below any applicable local minimum wage. An employee's position within the salary range will be based on several factors including, but not limited to, specific competencies, relevant education, qualifications, certifications, experience, skills, seniority, geographic location, performance, and business or organizational needs. #LI-Hybrid Position Information This is a Flexible Term appointment, which is for a definite period not to exceed six years. If final candidate is a Career Indefinite employee, Career Indefinite status may be maintained (should funding allow). Why Lawrence Livermore National Laboratory? - Included in 2026 Best Places to Work by Glassdoor! - Flexible Benefits Package - 401(k) - Relocation Assistance - Education Reimbursement Program - Flexible schedules (*depending on project needs) Security Clearance This position requires a Department of Energy (DOE) Q-level clearance. If you are selected, we will initiate a Federal background investigation to determine if you meet eligibility requirements for access to classified information or matter. Also, all L or Q cleared employees are subject to random drug testing. Q-level clearance requires U.S. citizenship. Pre-Employment Drug Test External applicant(s) selected for this position must pass a post-offer, pre-employment drug test. This includes testing for use of marijuana as Federal Law applies to us as a Federal Contractor. Wireless and Medical Devices Per the Department of Energy (DOE), Lawrence Livermore National Laboratory must meet certain restrictions with the use and/or possession of mobile devices in Limited Areas. Depending on your job duties, you may be required to work in a Limited Area where you are not permitted to have a personal and/or laboratory mobile device in your possession. This includes, but not limited to cell phones, tablets, fitness devices, wireless headphones, and other Bluetooth/wireless enabled devices. If you use a medical device, which pairs with a mobile device, you must still follow the rules concerning the mobile device in individual sections within Limited Areas. Sensitive Compartmented Information Facilities require separate approval. Hearing aids without wireless capabilities or wireless that has been disabled are allowed in Limited Areas, Secure Space and Transit/Buffer Space within buildings. Equal Employment Opportunity We are an equal opportunity employer that is committed to providing all with a work environment free of discrimination and harassment. All qualified applicants will receive consideration for employment without regard to race, color, religion, marital status, national origin, ancestry, sex, sexual orientation, gender identity, disability, medical condition, pregnancy, protected veteran status, age, citizenship, or any other characteristic protected by applicable laws.