Data Scientist

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!

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.

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.

• 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.

• 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.

• 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.

• Bring current knowledge of AI and data science methods, including frontier models, open-source models, enterprise AI platforms, and applied machine learning practices.
• Help leaders understand where AI and machine learning can create value, where traditional analytics is the better answer, and where foundational data work is required.
• Recommend practical approaches that balance speed, accuracy, cost, governance, security, and business impact.

• Demonstrable results delivering data science, machine learning, analytics, or AI projects that improved business processes, decisions, operations, customer outcomes, or revenue outcomes.
• Strong technical foundation in Python, SQL, applied statistics, machine learning, model evaluation, feature engineering, experimentation, and data analysis.
• Hands-on experience building models or AI-enabled data products in a business environment.
• Experience working with structured and unstructured enterprise data.
• Experience partnering with business stakeholders to define problems, interpret results, explain tradeoffs, and support adoption.
• Ability to operate with imperfect data, make practical tradeoffs, and iterate based on real business feedback.

• Experience with Snowflake, including analytics, data engineering, machine learning, Cortex, governed data access, or enterprise-scale data products.
• Experience with modern data and AI platforms such as Databricks, ClickHouse, Spark, cloud AI/ML services, or comparable environments.
• Experience with generative AI and LLM application patterns such as RAG, embeddings, semantic search, prompt design, Text-to-SQL, agents, orchestration, and evaluation.
• Experience with model deployment, monitoring, observability, reproducible workflows, version control, documentation, and responsible AI controls.
• Experience working across business, IT, Data Engineering, Analytics, Data Science, and AI delivery teams.

Success means the Data Scientist helps the business use AI, machine learning, and corporate data to make better decisions and improve how work gets done. The role should produce models, analyses, AI-enabled applications, and data products that are adopted by business teams, improve measurable outcomes, and create repeatable patterns we can scale across the company.