• Analyze and model FEA-derived engineering datasets • Apply uncertainty quantification techniques • Implement active learning strategies • Collaborate closely with mechanical engineers • Select, justify and implement appropriate ML approaches • Build, validate and iterate on models using Python in an AWS environment • Work semi-autonomously

• Work on end‑to‑end classification and forecasting use cases, including problem framing, data preparation, model development, evaluation, and basic deployment support (e.g., demand forecasting, churn prediction). • Explore and clean data; perform Exploratory Data Analysis (EDA) to understand datasets and identify data quality issues. • Engineer features for tabular and time‑series data. • Train, validate, and tune standard Machine Learning models (e.g., logistic regression, decision trees, ensemble methods, gradient boosting, classical time‑series models, simple neural networks). • Evaluate models using appropriate metrics with clear impact on business KPIs. • Build clear visualizations and deliver concise reports to present insights and model results to business stakeholders. • Collaborate with data engineers and AI engineers to bring models to production (batch scoring, APIs, monitoring, dashboards). • Document data sources, modeling assumptions, and experiment results in a reproducible manner (notebooks, reports, wikis). • Translate business needs into technical goals by defining success metrics, auditing data feasibility, and aligning with stakeholder expectations. • Participate in pre‑sales activities (for senior consultant level).

• Leading the design, development, and deployment of AI/ML models for remote diagnostics, predictive maintenance, and operational optimization. • Analyzing large-scale machine and service datasets to uncover actionable insights and inform product improvements. • Collaborating with cross-functional teams including engineering, product management, and MLOps to integrate AI solutions into commercial applications. • Appling statistical, machine learning, and optimization techniques to solve complex healthcare challenges. • Developing and operationalizing GenAI solutions, including RAG architectures and AI agents using AWS, Azure, and open-source tools. • Ensuring scalability, reusability, and high-quality standards across AI products and pipelines. • Communicating technical findings and strategic recommendations to stakeholders across business and technical domains. • Mentoring junior team members and promote a culture of data-driven decision-making and continuous learning.

• Create the analytical foundation that powers pricing decisions across the company. • Develop frameworks and datasets that allow teams to understand price sensitivity, margin impact, and marketplace behaviour. • Build visibility into how pricing performs across different product categories and services. • Introduce experimentation into pricing decisions. • Design and analyse experiments that measure elasticity, conversion impact, and behavioural changes across pricing structures. • Work with Product and Engineering across all domains to implement safe and scalable experimentation. • Ensure pricing decisions across teams work together. • Work with leaders across Marketplace, Fulfilment, and Fintech to identify inconsistencies, conflicts, and opportunities. • Create a shared framework that helps teams understand trade offs between margin, liquidity, and growth. • Partner with Data and Engineering to move pricing logic into scalable systems. • Define the models, signals, and infrastructure required for dynamic or rules based pricing. Ensure pricing inputs can be integrated into the product platform over time. • Translate complex analysis into decisions leaders can act on. • Provide clear insight into pricing behaviour across the platform. • Help teams understand the trade offs between margin, liquidity, growth, and customer behaviour.