Job Opportunities API

Why Choose Bottomline? Are you ready to transform the way businesses pay and get paid? Bottomline is a global leader in business payments and cash management, with over 35 years of experience and moving more than $16 trillion in payments annually. We're looking for passionate individuals to join our team and help drive impactful results for our customers. If you're dedicated to delighting customers and promoting growth and innovation - we want you on our team! The Opportunity We are hiring a Data Scientist to help transform how the business uses AI, machine learning, and corporate data to improve decisions, workflows, and outcomes. This role will partner directly with business teams and, in many cases, will be forward deployed into the business. The Data Scientist will use the full data science toolkit, including statistical modeling, machine learning, generative AI, optimization, experimentation, forecasting, entity resolution, and advanced analytics, to deliver results. The right candidate understands both the technical depth of data science and the operating reality of the business, selects the right method for the problem, and converts solutions into measurable value. How the Role Works The Data Scientist will move from business problem to usable solution through a practical delivery model: Understand the business outcome, workflow, decision, or constraint that needs to improve. Assess available data, data quality, system context, technical feasibility, and governance requirements. Select the right approach across statistics, machine learning, generative AI, optimization, simulation, or advanced analytics. Build models, prototypes, analyses, and AI-enabled data products that can be tested with users. Evaluate accuracy, reliability, business relevance, operating fit, and measurable impact. Partner with Data Engineering, Analytics, IT, AI Solutions Engineering, and business owners to operationalize what works. What You Will Do Applied Data Science and Modeling Build models and analytical solutions using machine learning, statistical modeling, forecasting, classification, clustering, anomaly detection, recommendation methods, entity matching, optimization, and causal or experimental analysis where appropriate. Apply generative AI and LLM-based methods where they are the right fit, including summarization, extraction, semantic search, retrieval-augmented generation, Text-to-SQL, document intelligence, and agentic workflows. Work with structured and unstructured data, including operational data, customer data, product data, documents, transcripts, contracts, support interactions, and enterprise knowledge sources. Evaluate models and AI systems using practical criteria such as accuracy, precision and recall, relevance, groundedness, stability, explainability, safety, adoption, and business value. Business Partnership and Problem Definition Partner with business stakeholders to define the problem, clarify the desired outcome, and determine what decisions or workflows need to change. Translate ambiguous business questions into clear data science problem statements, solution approaches, and measurable success criteria. Communicate findings, model outputs, limitations, and recommendations in plain language that business and technical teams can act on. AI, Data Platform, and Delivery Work in a Snowflake-centered data environment and use its capabilities for analytics, data engineering, machine learning, AI, governed data access, and enterprise-scale data products where appropriate. Build reusable patterns for models, prompts, features, evaluation, monitoring, and data products that can scale beyond a single use case. Partner with technical teams to move solutions from prototype to production use with appropriate controls, documentation, monitoring, and responsible AI practices. Thought Leadership Bring current knowledge of AI and data science methods, includi