• 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

• Collect, analyze, and interpret small/large datasets to uncover meaningful insights to support the development of statistical methods / machine learning algorithms. • Lead the design, training, and deployment of NLP and transformer-based models for financial surveillance and supervisory use cases (e.g., misconduct detection, market abuse, trade manipulation, insider communication). • Development of machine learning models and other analytics following established workflows, while also looking for optimization and improvement opportunities • Data annotation and quality review • Exploratory data analysis and model fail state analysis • Contribute to model governance, documentation, and explainability frameworks aligned with internal and regulatory AI standards. • Client/prospect guidance in machine learning model and analytic fine-tuning/development processes • Provide guidance to junior team members on model development and EDA • Work with Product Manager(s) to intake project/product requirements and translate these to technical tasks within the team’s tooling, technique and procedures • Continued self-led personal development