• Provides Global Feasibility business deliverables while working independently with minimal oversight, across all indications. • Plans, prioritizes, organizes and completes work effectively to meet established objectives. • Responsible for research and analysis of data to support the development of country and site strategies for proposal and independent project feasibility assessments, as well as troubleshooting project issues. • Provides additional informatics and intelligence around patient access and country suitability, competitive environment, enrollment analysis, and supportive enrollment modelling. • Attends strategy calls and liaises with Business Unit representatives, Medical Directors, Proposal Analysts, and Business Development to review sponsor requirements and protocols for Requests for Proposals (RFPs) and Requests for Information (RFIs). • May act as departmental Subject Matter Expert (SME) for tools, processes and may act as Lead for departmental development initiatives.

• Applies data science and machine learning to the analysis of radio frequency and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights. • Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions across industrial, communications, and defense-adjacent applications. • Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking. • Design and develop signal processing pipelines and machine learning models that operate on RF, acoustic, and time-series sensor data, including beamforming, BSS, spectral subtraction, matched filtering, wavelet decomposition, and time-frequency analysis techniques. • Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable. • Perform exploratory data analysis, feature engineering, and signal feature extraction on raw demodulated RF and acoustic data to surface patterns and anomalies. • Analyze and interpret signals from various electrical asset monitoring systems utilizing RF, acoustic, and signal processing expertise to support fault isolation and anomaly detection. • Use asset monitoring sensor data as measurement to characterize and validate signal data. • Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and LRU level — identifying root causes from spectral, RF, and acoustic sensor data in complex industrial systems. • Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments. • Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions. • Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders. • Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

• Oversee the data management aspects of clinical trials, ensuring the accuracy and integrity of crucial information • Work in close collaboration with the Clinical project managers on the data management aspects of a clinical trial • Development, validation and maintenance of project specific databases • Design data transfer specifications together with our different customers • Programming and validation of data exports using SQL, R, … • Guarantee the quality and timely delivery of each data transfer

• Design, train, and deploy production ML models for core Sales Cloud use cases: opportunity scoring, deal-risk detection, and seller-facing explainable insights • Build and operationalize agentic workflows on Agentforce, including LLM-as-judge evaluation frameworks and agentic evaluation metrics • Partner closely with Product Management to help shape and drive the roadmap — bringing engineering perspective to prioritization and feasibility decisions • Explore and adopt new technologies: improved forecasting methods, RAG architectures, prompt tuning strategies, and emerging AI tooling • Mentor and coach junior engineers on the team