Build AI-powered finance applications using Databricks, Python, LLMs, and governed financial data. Develop tools for variance commentary, financial close checklists, reconciliation, FP&A, natural-language data exploration, and financial analysis. Partner with finance stakeholders, measure adoption and productivity gains, and maintain governance separating AI recommendations from financial controls. The role requires software engineering, finance process knowledge, and experience with Databricks, LLM applications, finance systems, and production-ready applications. The summary above was generated by AI Who we are We're a global, midsize CRO that pushes boundaries, innovates and invents because the path to a cure for the world's most persistent diseases is not paved by those who play it safe. It is built by those who take pioneering, creative approaches and implement them with quality and excellence. We are Worldwide Clinical Trials, and we are a global team of over 3,500+ experts, bright thinkers, dreamers and doers and, together, we are changing the way the world experiences CROs - in the best possible way. Our mission is to work with passion and purpose every day to improve lives and we are looking for others who value this same pursuit. Why Worldwide We believe everyone plays an important role in making a world of difference for patients and their caregivers. From our hands-on, accessible leaders, to our cohesive and supportive teams, we are committed to enabling professionals from all backgrounds and experiences to succeed. We prioritize cultivating a diverse and inclusive environment that continues to promote collaboration and creativity. We are proud to be a workplace where people thrive by being themselves and are inspired to do their best work every day. Join us! Make an Impact as a Forward Deployed Engineer (Finance Focused) at Worldwide Help transform how Finance teams work by building AI-powered tools that give analysts valuable time back. In this role, you'll partner closely with finance stakeholders to create practical solutions that accelerate close activities, improve financial analysis, and make financial data easier to access and understand. You'll work at the intersection of finance, data, and AI, developing tools that assist with variance commentary, financial planning and analysis, and close processes while maintaining strong governance and compliance standards. Your work will help finance professionals focus on higher-value analysis and decision-making. This is an opportunity for someone who combines finance expertise with software engineering skills and enjoys solving complex business challenges through modern data and AI technologies. Where You'll Work This is a REMOTE role open to candidates based in US. You will collaborate with global teams across multiple time zones. What You'll Do Build AI-assisted variance commentary and close-checklist solutions that generate draft insights from financial data and identify key business drivers. Develop experiences using Databricks Genie and governed finance data to enable analysts to explore information through natural language. Partner with FP&A Analysts and Project Financial Analysts to deliver tools that improve efficiency and enhance financial decision-making. Measure and report solution adoption, usage, and productivity gains to demonstrate business impact. Create assistive reconciliation capabilities that support finance teams while preserving human ownership of controls and approvals. Design and deliver AI-enabled applications using Databricks, Python, and modern LLM technologies. Ensure solutions align with governance requirements by maintaining clear separation between AI-generated recommendations and financial control activities. Collaborate with finance stakeholders to understand processes, close cycles, planning activities, and reporting needs. What You'll Bring Bachelor's degree in Finance, Accounting, Computer Science, Information Systems, Engineering, or a related field, or equivalent relevant experience. 2+ years of hands-on experience with Databricks and building data-driven solutions. 2+ years of LLM application engineering experience, including prompt engineering, context engineering, retrieval, structured outputs, and evaluation. 2+ years of experience using Python and sufficient full-stack development skills to deliver production-ready applications. 2+ years of experience working within or closely alongside Finance teams, with knowledge of FP&A processes, monthly close activities, budgeting, forecasting, variance analysis, and revenue recognition fundamentals. Experience working with finance systems and governed financial data, including platforms such as NetSuite, Certinia, and Workday Adaptive Planning. Familiarity with Microsoft 365 and Copilot, MLflow, Databricks Vector Search, Databricks Genie, Genie Code, and/or GenieOne; CRO or pharma-services experience and SOX exposure are preferred. Proficiency in English At Worldwide Clinical Trials, we are committed to fostering an inclusive and equitable workplace by providing transparent compensation. The salary range for this position is annually (For hourly paid positions, the stated salary range reflects hourly rates): United States of America - $121,500.00 - $240,500.00 The salary range provided is intended to give candidates an understanding of potential earnings. Compensation will fall within the provided range, and it not a guarantee of exact salary. The final salary will be determined based on relevant experience, performance, education, and internal equity. In addition to base salary, we offer a competitive benefits package depending on location. We ensure pay equity and transparency and comply with all applicable labor laws. Salary information will be discussed during the interview process, and we encourage candidates to inquire about compensation at any stage. We love knowing that someone is going to have a better life because of the work we do. To view our other roles, check out our careers page at Discover a world of difference at Worldwide ! For more information on Worldwide, visit www.

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