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Vetted Data Analysts
Pre-screened and vetted.
Mid-Level Software Development Engineer specializing in AWS and demand forecasting
Junior Machine Learning Engineer specializing in benchmarking, NLP, and computer vision
Senior Product Manager specializing in B2B SaaS MarTech/AdTech and AI automation
Senior Business Data Analyst specializing in financial services customer analytics
Mid-level Machine Learning Engineer specializing in LLMs and ML at scale
Mid-level Machine Learning & Biostatistics professional specializing in healthcare AI
Staff/Lead AI Engineer specializing in LLMOps, RAG, and Azure-based ML platforms
Mid-level Data Scientist specializing in geospatial computer vision and ML
Senior Python/ML Engineer specializing in LLM-powered backend systems
Mid-level Data Analyst & ML Engineer specializing in GenAI, NLP, and cloud data pipelines
Mid-level Data Scientist specializing in customer analytics, ML pipelines, and churn forecasting
Intern Data Scientist specializing in NLP and Large Language Models
Junior AI Product Engineer specializing in LLM workflows and analytics automation
Mid-level Data Scientist specializing in marketing analytics and scalable data platforms
Murali Krishna Vempalli
Mid-level Data Analyst specializing in retention, churn, and customer analytics
“Analytics professional with experience across healthcare and fintech, including building SQL/Python data pipelines at Optum and owning a fraud detection initiative at Razorpay. Stands out for combining messy-data cleanup, reproducible analytics workflows, and stakeholder-driven metric design, with a reported 25% improvement in fraud detection while keeping false positives under control.”
Priyanshu Maurya
Mid-level Data Scientist specializing in insurance, finance, and healthcare analytics
“Built and productionized LLM-driven sentiment scoring for earnings call transcripts at Goldman Sachs, replacing legacy NLP to deliver a cleaner trading signal while managing latency/cost via batching, caching, and distilled models. Also implemented an Airflow-orchestrated fraud modeling pipeline at MetLife with drift-based retraining and SageMaker deployment, and has a disciplined evaluation/rollout framework for reliable AI workflows.”