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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Advanced capability in strategic workforce planning, succession planning, organization design, and leadership development within complex, fast-changing environments. Strong business acumen with the ability to connect technology strategy, external market trends, and talent implications into clear executive recommendations. Highly effective business acumen and ability to link business outcomes and external market trends to talent strategies. Very effective at making connections between broader SBU/enterprise goals and talent strategies and to develop strategies/plans to solve talent trends or issues (leveraging the right resources). Expert strategic thinking and problem-solving skills to address complex challenges across multiple stakeholder groups. 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In-depth knowledge of IT concepts, strategies and methodologies and their application to business opportunities. In-depth knowledge of business operations, objectives and strategies. Advanced knowledge of management concepts, practices and techniques. Ability to promote a team environment consisting of several teams Knowledge of Financial concepts and the insurance industry, including familiarity with Premium & Losses and managing risk. Highly developed negotiation, facilitation and consensus building skills. 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Strong SQL skills required with a familiarity of financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques preferred. Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred. Solid understanding of financial markets and multi-asset investment risk domain. Practical experience in developing and maintaining models, tools, and reports that showcase a deep understanding of quantitative techniques, methods, statistics and econometrics Excellent problem-solving skills, with the ability to think critically, independently Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders. Strong attention to detail, organization, and the ability to manage multiple tasks and priorities in a fast-paced environment. Full-stack development knowledge with a minimum of 5 years professional experience programming in Python demonstrating the ability to write efficient and robust code able to process and analyze large financial datasets. Experience with key Python Libraries (pandas, NumPy) required Experience in front-end development and user experience (UX) design required; experience with Pythonic front-end and data visualization libraries (e.g., Plotly, Dash) preferred. Experience using Version Control (Git) required. Proven ability to design, build, and scale application systems in data-rich environments. Strong SQL skills required with a familiarity of financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront), financial databases, and data manipulation techniques preferred. Experience with statistical and time-series data analysis using pythonic libraries (such as Scikit-Learn, SciPy, cvxpy) is preferred. 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Liaise with Compliance, Privacy, and central GenAI teams. Continuously scan the Gen AI landscape: model releases, orchestration frameworks, prompt-engineering trends?and assess fit for our roadmap Establish engineering standards for code quality, observability, cost-efficient LLM usage, and secure development Mentor MLOps engineers and full-stack developers; foster a dynamic, collaborative culture that earns trust across technical and business teams Design of end-to-end solutions as an architect or senior engineer; proven Tech Lead for small, agile teams and the ability to work through others Ability to communicate complex AI concepts to non-technical stakeholders and influence decision-makers with a calm, measured style. 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Partner with quantitative analysts and portfolio strategists to convert AI-generated prototypes and research artifacts into robust, production-ready applications. Build and maintain data pipelines for portfolio and market data across public and private markets, leveraging the LMI data ecosystem. Contribute to the team's shared quantitative library, supporting both existing and new models, analytics, and reusable components. Support and extend existing applications and dashboards in production, and drive the design and implementation of new ones to meet evolving research and decision-support needs. Excellent problem-solving and analytical skills, with the ability to think critically, independently, and act with minimal handholding Effective communication skills, with the ability to clearly articulate complex ideas and analysis to both technical and non-technical stakeholders. 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Familiarity with financial data platforms (such as Bloomberg, FactSet, Aladdin, eFront, Moodys), financial databases, and data manipulation techniques strongly preferred. Strong quantitative background required, with hands-on experience applying statistical, time-series, and optimization techniques using Python libraries (e.g., SciPy, Scikit-Learn, cvxpy, statsmodels) to portfolio construction and asset allocation problems. Deep understanding of investment management and portfolio composition strategies, with knowledge across asset classes including fixed income, public and private equity, and private credit. Demonstrated experience applying quantitative methodologies (including optimization, factor modeling, risk decomposition, and econometrics) to support portfolio construction, asset allocation, and investment decisions. Demonstrated ability to partner with business teams to convert AI-generated artifacts (e.g., prototypes, proofs of concept) into production-ready applications. Experience bridging the gap between rapid AI-developed prototypes to quality full-stack applications is highly valued. Proven ability to design, build, and scale application systems in data-rich environments including custom AI tools. Master's or Ph.D. (or equivalent experience) in a quantitative or engineering field such as Computer Science, Mathematics, Statistics, Econometrics, Financial Engineering, or a related discipline. 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Systems-level engineer: Working hand-in-hand with your business partners, you'll develop technical requirements and design robust and flexible product and system architectures Forward thinker: Leading by example, you'll promote a culture of continuous improvement by finding solutions that increase efficiency, accelerate the adoption of new technologies and enhance profitability Ten years of related software engineering experience A comprehensive understanding of agile environments Extensive knowledge of contemporary frameworks and emerging technologies A history of practicing DevOps and SecOps A strong background in business operations and strategies, including knowledge of global business, technology trends and the financial services industry Solid negotiation, facilitation, consensus building and influencing capabilities Ability to identify patterns and problems across teams and deliver effective solutions Openness and adaptability to respond to fast-moving circumstances A design thinking and test-driven development mindset Excellent oral and written communication skills, and an aptitude for working in teams Knowledge Experience developing or architecting cloud-hosted, responsive web applications using Java, HTML5 and CSSS3 Strong background in code review High-level proficiency with client-side JavaScript frameworks, server-side frameworks, and NoSQL databases Familiarity with event-based System of Engagement architectures and integration with API exposing microservices Deep understanding of layered solutions and designs Cognizance of security concerns, from access control and authentication to secured SDLC processes Understanding of DCI/MDM systems, data ingestion and cloud-scale data sets A Bachelor's or Master's degree in a technical or business discipline, or equivalent experience delivering durable solutions that prevent recurrence and accelerate flow 1) Systemic Problem Identification & Prioritisation Continuously mine signals (test outcomes, pipeline telemetry, defect trends, environment incidents, release friction, manual handoffs) to surface recurring constraints and risks. Maintain a prioritised \"Top Problems\" backlog with clear problem statements, leading indicators, target outcomes, and measures of success. Design solution options spanning process, tooling, automation, and integration guardrails; quantify impact and align stakeholders. Deliver solutions directly (scripts/tools, pipeline improvements, readiness checks, dashboards) and/or drive implementation through partner teams. Improve assurance gate effectiveness and speed by shifting confidence earlier in the lifecycle and reducing reliance on slow, brittle checks. Implement and tune risk-based gating aligned to defined thresholds and acceptance criteria (what must block vs what must be visible). Improve diagnosability via consistent artifacts, enriched run context, and actionable runbooks/playbooks. Ensure AI usage follows LM safety/security procedures and includes appropriate guardrails (accuracy checks, auditability, data handling). Work with the wider Liberty ecosystem of architects and technical leaders to ensure solutions align with enterprise standards, tool selection direction, and quality governance. Contribute improvements back into shared standards and tooling where possible to maximise leverage and consistency across the program. 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Hands-on with product coverage, action, formula, and interface configuration. Experience in implementation projects or large enhancements. Strong knowledge of the reinsurance industry, including facultative, treaty, and ceded reinsurance. Advance Excel skills required. Strong negotiation, facilitation and consensus building skills. Strong oral and written communication skills; presentation skills. Extensive knowledge of the following: IT concepts, strategies, methodologies Extensive understanding of agile software development concepts and processes. Must be proactive, demonstrate initiative and be a logical thinker. Extensive consultative skills, including the ability to understand and apply customer requirements, including drawing out unforeseen implications and making recommendations for design, the ability to define design reasoning, understanding potential impacts of design requirements Extensive understanding of backlog tracking, burndown metrics, and incremental delivery. 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