Role Description This role is responsible for developing industrialized optimisation and machine learning models as part of a full-stack product squad that delivers operations decision-support software. As a key member of a product squad and reporting to the Lead Product Data Scientist, a Data Scientist will: - Develop data pipelines, machine learning models, and complex optimization models in the ODS software product suite. - Oversee modelling and robust implementation of features contributing to an operations decision-support product. - Ensure that features integrate seamlessly into the product’s technical stack (data ingestion, user interface, orchestration) as well as the business process and use case. Accountabilities - Understand a business problem and its component processes end to end, identifying opportunities to make decisions more optimally leveraging decision-support tooling. - Conduct analyses and visualizations to identify valuable opportunities for decision-support and determine trade-offs between different potential feature implementations. - Prototype advanced machine learning and optimization models to prove the value of a use case and approach (in Python). - Deliver features to industrialize machine learning and optimization models in Python using best-practice software principles. - Build automated, robust data cleaning pipelines that follow software best-practices (in Python). - Implement integrations between the core algorithm (machine-learning or optimization) and a workflow orchestration paradigm such as Dagster. - Implement software in a cloud-based deployment pipeline with Continuous Integration / Continuous Deployment (CI/CD) principles. - Build logging, error handling, and automated tests (e.g., unit tests, regression tests) to ensure the robustness of operationally critical decision-support products. - Deliver features to harden an algorithm against edge cases in the operation and in data. - Conduct analysis to quantify the adoption and value-capture from a decision-support product. - Engage with business stakeholders to collect requirements and get feedback. - Contribute to conversations on feature prioritisation and roadmap, understanding the trade-off between speed vs. long-term value. - Understand and integrate the product into existing business processes, contributing to the development and adoption of new business processes leveraging a decision-support product. - Communicate feature and modeling approach, trade-offs, and results with the internal team and business stakeholders. Skills/capabilities - Strong knowledge of machine learning and optimization techniques, including supervised (regression, tree methods, etc.), unsupervised (clustering) learning, and operations research (linear, mixed integer programming, heuristics). - Fluent in Python (required) and other programming languages (preferred) with strong skills in applying DS, ML, and OR packages (scikit-learn, pandas, numpy, gurobi, etc.) to solve real-life problems and visualize the outcomes (e.g., seaborn). - Proficient in working with cloud platforms (AWS preferred), code versioning (Git), and experiment tracking (e.g., MLflow). - Experience with cloud-based ML tools (e.g., SageMaker), data and model versioning (e.g., DVC), CI/CD (e.g., GitHub Actions), workflow orchestration (e.g., Airflow/Dagster), and containerized solutions (e.g., Docker, ECS) is nice to have. - Experience in code testing (unit, integration, end-to-end tests). - Strong data engineering skills in SQL and Python. - Proficient in the use of Microsoft Office, including advanced Excel and PowerPoint skills. - Advanced analytical skills, including the ability to apply a range of data science and analytic techniques to quickly generate accurate business insights. - Understanding of the trade-offs of different data science, machine learning, and optimization approaches, and ability to intelligently select the best candidates to solve a particular business problem. - Able to structure business and technical problems, identify trade-offs, and propose solutions. - Communication of advanced technical concepts to audiences with varying levels of technical skills. - Managing priorities and timelines to deliver features in a timely manner that meet business requirements. - Collaborative team-working, giving and receiving feedback, and always seeking to improve team processes. Qualifications - Master’s degree or greater in data science, ML, or operational research, or 2+ years of highly relevant industry experience (required). - 0-2 years working on production ML or optimization software products at scale (required). - Experience in developing industrialized software, especially data science or machine learning software products (preferred). - Experience in relevant business domains (transportation, airlines, operations, network problems) (preferred). Key interfaces - Lead Product Data Scientist - Other Data Scientists - Business stakeholders and users - Software engineers (front-end, back-end, DevOps, data engineers) - Product & change managers - BA Digital teams (e.g., architects, application support managers) - External partners and third parties, as required - ODS Leadership (Head of Data & Analytics, Head of iOps & Optimisation, Director of ODS) Key performance indicators - Model accuracy, performance, and runtime (precision, recall, accuracy) - Time to develop and deploy features and models - Data ingestion & processing efficiency and robustness - Code quality and robustness (e.g., unit test coverage) - Collaboration and cross-functional teamwork Behaviours and attitude - I’m a role model for all BA brand behaviours and ways of working – I walk the talk. - I exude a can-do attitude (best of BA). - I’m flexible and agile, always ready to adapt when things don’t go to plan. - I’m an ambassador for BA and my team. - I role model our Leadership Behaviours. Core traits - Systems thinking - Detail oriented while understanding the big picture - Curious, self-motivated, proactive, and action-oriented - Creative and innovative - Resilient and flexible in light of changing priorities and approaches - Data-driven - Pragmatic - Collaborative - A true believer in the power of using data to drive better decision making - A technologist, interested in keeping up with the latest and greatest in software development, optimization, and machine learning - Commitment to delivering business value Interview Questions Ask the candidate the following questions and share the summary along with the candidate resume: - Tell me about an optimisation problem you have solved? - What was the business problem and the context of it? - Explain what the measure was? - What were the constraints? - Which algorithm did you use to solve this? - What was your end result?

Senior AI Data Scientist Location: Remote - USA, Europe, Israel Compensation: $130K - $150K We are hiring on behalf of our client, a leading financial technology firm building the infrastructure for safer, more accessible global markets. Their risk management systems, oracles, and AI models secure hundreds of billions in assets and process trillions in transaction volume across major protocols. They recently launched a cutting-edge financial intelligence platform that transforms complex market data into actionable insights, bringing institutional-grade intelligence to a global audience. They are seeking a Senior AI Data Scientist to lead the design, evaluation, and evolution of the agentic systems behind their financial intelligence platform. This role sits at the intersection of LLMs, financial data, and decision systems. You will own the logic behind how their models reason over data, how agents coordinate and make decisions, and how they rigorously measure quality, correctness, and risk in production AI workflows. You’ll work closely with product, engineering, and research teams to move from experimentation to reliable, scalable AI systems that operate under real-world financial constraints. Key Responsibilities: - System Design: Design and own single and multi-agent systems that reason, plan, and act over complex financial workflows. - Agent Logic: Define agent behavior, memory, and tool-use strategies with a strong emphasis on correctness and controllability. - Evaluation Frameworks: Develop and maintain LLM evaluation frameworks covering accuracy, faithfulness, latency, cost, regressions, and edge cases. - Production Implementation: Design structured prompting, schemas, and tool-calling strategies; build and operate MCP servers including schema design and safety boundaries. - Optimization: Analyze model behavior and failure modes to turn qualitative issues into measurable signals; optimize performance and cost across workflows. - Mentorship: Mentor engineers and data scientists, setting best practices for applied LLM and agentic systems. Interview Process: - Recruiter / HR Call - 30min screen - Hiring Manager Interview - 30min technical screen - Technical Interview - 1-hour technical interview focused specifically on the "coding" elements eg Python and its libraries and machine learning - Technical Interview - 1-hour technical interview focused specifically on AI elements of the role (this will be more data science in nature) - Founder / CEO Interview - 30mins

Senior AI Data Scientist Location: Remote - USA, Europe, Israel Compensation: $130K - $150K We are hiring on behalf of our client, a leading financial technology firm building the infrastructure for safer, more accessible global markets. Their risk management systems, oracles, and AI models secure hundreds of billions in assets and process trillions in transaction volume across major protocols. They recently launched a cutting-edge financial intelligence platform that transforms complex market data into actionable insights, bringing institutional-grade intelligence to a global audience. They are seeking a Senior AI Data Scientist to lead the design, evaluation, and evolution of the agentic systems behind their financial intelligence platform. This role sits at the intersection of LLMs, financial data, and decision systems. You will own the logic behind how their models reason over data, how agents coordinate and make decisions, and how they rigorously measure quality, correctness, and risk in production AI workflows. You’ll work closely with product, engineering, and research teams to move from experimentation to reliable, scalable AI systems that operate under real-world financial constraints. Key Responsibilities: - System Design: Design and own single and multi-agent systems that reason, plan, and act over complex financial workflows. - Agent Logic: Define agent behavior, memory, and tool-use strategies with a strong emphasis on correctness and controllability. - Evaluation Frameworks: Develop and maintain LLM evaluation frameworks covering accuracy, faithfulness, latency, cost, regressions, and edge cases. - Production Implementation: Design structured prompting, schemas, and tool-calling strategies; build and operate MCP servers including schema design and safety boundaries. - Optimization: Analyze model behavior and failure modes to turn qualitative issues into measurable signals; optimize performance and cost across workflows. - Mentorship: Mentor engineers and data scientists, setting best practices for applied LLM and agentic systems. Interview Process: - Recruiter / HR Call - 30min screen - Hiring Manager Interview - 30min technical screen - Technical Interview - 1-hour technical interview focused specifically on the "coding" elements eg Python and its libraries and machine learning - Technical Interview - 1-hour technical interview focused specifically on AI elements of the role (this will be more data science in nature) - Founder / CEO Interview - 30mins

Senior AI Data Scientist Location: Remote - USA, Europe, Israel Compensation: $130K - $150K We are hiring on behalf of our client, a leading financial technology firm building the infrastructure for safer, more accessible global markets. Their risk management systems, oracles, and AI models secure hundreds of billions in assets and process trillions in transaction volume across major protocols. They recently launched a cutting-edge financial intelligence platform that transforms complex market data into actionable insights, bringing institutional-grade intelligence to a global audience. They are seeking a Senior AI Data Scientist to lead the design, evaluation, and evolution of the agentic systems behind their financial intelligence platform. This role sits at the intersection of LLMs, financial data, and decision systems. You will own the logic behind how their models reason over data, how agents coordinate and make decisions, and how they rigorously measure quality, correctness, and risk in production AI workflows. You’ll work closely with product, engineering, and research teams to move from experimentation to reliable, scalable AI systems that operate under real-world financial constraints. Key Responsibilities: - System Design: Design and own single and multi-agent systems that reason, plan, and act over complex financial workflows. - Agent Logic: Define agent behavior, memory, and tool-use strategies with a strong emphasis on correctness and controllability. - Evaluation Frameworks: Develop and maintain LLM evaluation frameworks covering accuracy, faithfulness, latency, cost, regressions, and edge cases. - Production Implementation: Design structured prompting, schemas, and tool-calling strategies; build and operate MCP servers including schema design and safety boundaries. - Optimization: Analyze model behavior and failure modes to turn qualitative issues into measurable signals; optimize performance and cost across workflows. - Mentorship: Mentor engineers and data scientists, setting best practices for applied LLM and agentic systems. Interview Process: - Recruiter / HR Call - 30min screen - Hiring Manager Interview - 30min technical screen - Technical Interview - 1-hour technical interview focused specifically on the "coding" elements eg Python and its libraries and machine learning - Technical Interview - 1-hour technical interview focused specifically on AI elements of the role (this will be more data science in nature) - Founder / CEO Interview - 30mins