• Develop and implement real-time scoring models to quantify the risk level of transactions and credit operations. • Build predictive models using internal and third-party data to optimize user onboarding and reduce losses. • Evolve static rule engines into dynamic, graph-based ones, enabling more intelligent and adaptable rule management. • Lead the adoption of new technologies like Databricks and Data Catalogue, advocating for best practices and facilitating a transition to a more modern and efficient data environment. • Analyze large volumes of transactional, user behavior, and demographic data to identify patterns, trends, and opportunities for improvement in risk assessment. • Develop and implement fingerprinting and geographic tracking solutions to improve risk assessment. • Guide and mentor team members, sharing your experience and knowledge, and leading key projects from a technical perspective. • Monitor and analyze the performance of credit models, focusing on their stability and accuracy.

• Support the maintenance and enhancement of R Markdown reports and Shiny apps • Contribute to clear and well-documented analytical deliverables (code comments, README files, and basic user documentation) • Collaborate with cross-functional team members to understand analytical requirements and incorporate feedback • Apply standard practices for reproducible research, version control, and organized project structure • Practical experience using R and RStudio in an enterprise data science setting • Exposure to reproducible reporting, structured analytical workflows, and version control practices • Introduction to enterprise analytics, development, and AI-enabled productivity tools (e.g., Posit environment, Azure DevOps, Microsoft CoPilot) • Insight into how data science supports clinical development and evidence-based decision-making • Ongoing mentorship and guidance from senior data scientists within Global Biometrics.

About the Role Fetch is looking for a Data Science Manager to lead a team of data scientists and analysts focused on high-impact product and business problems across the consumer app experience. In this role, you will serve as the data lead for a pillar, partnering closely with Product and Engineering to shape priorities, guide technical strategy, and ensure the team is delivering meaningful business outcomes. This is a hands-on leadership role for someone who can operate strategically and technically. You will help the team navigate ambiguity, break down broad problem spaces into clear analytical approaches, and drive high-quality work that informs product direction. You should be comfortable coaching data scientists on experimentation, modeling, and stakeholder communication, while also evaluating technical proposals and presenting recommendations to senior leadership. The ideal candidate brings strong people leadership, deep data science expertise, and the ability to influence cross-functional partners at a high level. This person will play a key role in helping Fetch continue to build a strong, high-impact generalist data science organization. What you’ll do at Fetch: Team Leadership & Development - Lead, coach, and develop a team of data scientists focused on high-impact product and business opportunities. - Provide clear feedback, support career growth, and create an environment that enables strong performance, healthy team dynamics, and continued development. - Support hiring, onboarding, and team growth while contributing to the broader evolution of Fetch’s data science organization. Strategic Direction & Business Impact - Serve as the data lead for a product pillar, partnering closely with Product and Engineering to shape priorities and influence strategic direction. - Translate ambiguous business and product questions into clear analytical roadmaps that drive measurable impact across user growth, customer value, revenue, and product performance. - Ensure the team’s work is aligned to broader business goals and meaningfully informs product roadmap decisions. Technical Leadership & Experimentation - Guide the team in experimentation, measurement, statistical analysis, causal inference, and predictive modeling with a high bar for rigor and practical application. - Evaluate analytical strategies, technical proposals, and team outputs to ensure work is high quality, actionable, and grounded in strong scientific thinking. - Help break down broad problem spaces into clear technical approaches and support execution from concept through impact. Cross-Functional Collaboration & Influence - Partner closely with Product, Engineering, Analytics, Marketing, and business leaders to turn insights into decisions, systems, and shipped outcomes. - Communicate findings, recommendations, and tradeoffs clearly to senior and executive leadership to influence direction and drive alignment. - Create clarity in ambiguous, fast-moving environments by setting priorities, managing dependencies, and protecting the team from unnecessary thrash. Operational Excellence - Establish strong standards for quality, prioritization, and execution across the team’s work. - Balance speed and rigor by making thoughtful tradeoffs that maintain momentum while protecting scientific quality. - Contribute to a strong generalist data science organization through improvements to team operations, planning, and ways of working. Minimum Requirements: - 6+ years of experience in data science, applied analytics, or a related quantitative field. - 1+ year of direct people management experience leading data scientists or other quantitative practitioners. - Hands-on experience in experimentation, statistical analysis, causal inference, measurement, and predictive modeling. - Proven ability to partner cross-functionally with Product and Engineering to define priorities and translate ambiguous business problems into clear analytical approaches. - Demonstrated ability to connect technical work to measurable business outcomes such as revenue growth, user growth, engagement, retention, or customer value. - Experience evaluating analytical approaches, guiding technical strategy, and driving high-quality, actionable outputs. - Strong written and verbal communication skills, including experience presenting findings and recommendations to senior stakeholders. - Proficiency in SQL, Python, and modern data science workflows. Preferred Requirements: - Experience leading data scientists in a consumer product, marketplace, or growth-focused environment. - 2+ years of people management experience. - Experience serving as the primary data lead for a product area, product pillar, or business domain. - Track record of influencing product strategy and roadmap decisions through data-driven recommendations. - Experience operating effectively in highly ambiguous environments and creating structure, focus, and clarity for teams. - MBA, consulting background, or comparable experience driving strategic decision-making across cross-functional stakeholders. - Prior hands-on experience as a data scientist, with a strong preference for candidates who have directly led data science work. - Experience mentoring generalist data science teams across a broad range of product and business problem spaces. This is a full-time role that can be held from one of our US offices or remotely in the United States. Compensation: At Fetch, we offer competitive compensation packages including base, equity, and benefits to the exceptional folks we hire. Discover our benefits and how our employees live rewarded at https://fetch.com/careers.

• Design and deliver production-grade agentic systems, from multi-agent orchestration to the AI services and models that power them • Assess fit of agentic solutions per use case, being intentional in system design • Design the most suitable orchestration pattern per use case • Evaluate and recommend the best suited agent development framework • Engineer multi-step agent workflows that are reliable, auditable and modular, supporting both linear and non-linear control flows • Implement agent memory and state management systems • Optimise context assembly for each agent interaction • Evaluate and select the most appropriate foundation models, embeddings and context-specific model variants; apply fine-tuning or adaptation where needed • Implement guardrails to ensure operation within ethical, legal, brand boundaries • Build services for agent use, including ML models, GenAI and task-specific APIs • Process multi-modal data using appropriate embeddings and vector retrieval • Build, evaluate and maintain robust RAG and / or GraphRAG pipelines • Implement fallback strategies (e.g., retries, backup tools, escalation, safe exits) • Build and integrate MCP servers to expose and consume data and AI services • Use Google's A2A protocol to support independent agent communication, designing composable service boundaries and appropriate integration patterns • Containerise applications (Docker) – our platform team manages K8s • Apply AgenticOps practices, including evaluation, observability, guardrails, security, performance optimisation, CI/CD, versioning, rollback, drift detection • Design and implement testing strategies for non-deterministic behaviour • Stay abreast of the latest AI research trends and integrate them into our products • Maintain documentation for architectures, APIs and operational procedures • Communicate progress, blockers, technical approaches, challenges and potential solutions to technical colleagues and non-technical stakeholders.