• Shape and implement the company's data science strategy and drive innovation. • Identify opportunities to leverage data for business value and impact. • Take ownership of the full technical stack for development and deployment. • Design, build, and maintain scalable machine learning models and efficient data pipelines. • Ensure robust data management and engineering practices. • Collaborate with cross-functional teams to gather requirements and deliver insights. • Communicate complex technical concepts clearly to both technical and non-technical stakeholders.

• Leverage advanced algorithms and machine learning techniques to transform visual data into actionable insights • Design and implement robust models to analyze and interpret images and videos • Collaborate closely with cross-functional teams to integrate these capabilities into products • Preprocess large datasets and deploying machine learning frameworks • Conduct experiments to refine model accuracy and present findings to stakeholders

• Design and develop ML models (e.g., classification, ranking, scoring) that power customer-facing features • Use SQL and Python (pandas, scikit-learn) to analyse product data and identify opportunities • Rapidly prototype new AI/ML-driven product ideas in collaboration with Product and Design • Define hypotheses, evaluate model performance, and iterate quickly • Collaborate with Engineering to productionise validated models • Improve automation, personalisation, and predictive capabilities within the product • Ensure models are measurable, interpretable, and aligned with user value • Assess and validate internal and external data sources used in ML features • Evaluate signal quality, bias, completeness, and reliability • Define metrics and checks to ensure data is fit for customer-facing use • Move from ambiguous problem → prototype → shipped feature with strong ownership

• Develop and deploy new AI methods and systems to solve complex defense challenges. • Build AI models, heuristics, and agentic solutions from the ground up. • Work directly with customers to translate vague mission needs into concrete AI workflows and technical requirements. • Take full accountability for client problems, from initial discovery to deploying production code into AI systems. • Partner with AI platform and data engineering teams to build end-to-end production solutions. • Wrap specific Data Science solutions into reusable agentic AI frameworks and features for broader deployment. • Create agentic AI systems using AI orchestrators and advanced reasoning capabilities.