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Build statistical and ML models for healthcare analytics and pharma marketing mix modeling to optimize spend and strategy using Python, SQL, and cloud tools.
Build and deploy AI/ML models for healthcare claims processing using PyTorch/TensorFlow, MLOps pipelines, and cloud tools to reduce payment inaccuracies and waste.
Build and optimize AWS-based ETL/ELT pipelines using Redshift, S3, Glue, and Lambda to move and transform data for analytics and AI/ML workloads.
Principal ML Engineer designs, builds, and deploys production-grade AI systems using LLMs and deep learning, integrating agent workflows across GCP, AWS, and Azure.
Lead a team building generative AI and agentic systems for healthcare, including LLMs, RAG, and AI agents, from prototyping to production with MLOps and Responsible AI practices.
Build, train, and deploy ML models using Python and frameworks like TensorFlow/PyTorch to power AI-driven business solutions.
Design and build ML models (e.g., House Pricing Index) to generate predictive insights from real-estate and property data using Python, SQL, and cloud tools.
Designs and deploys advanced ML models to extract insights from large property datasets, using Python, PyTorch, and cloud platforms to power real-estate intelligence.
Build and deploy ML models to predict ad-campaign delivery outcomes (reach, frequency, risk) for Netflix’s new ad-supported tier, replacing a simulation engine with fast, interpretable supervised models.
Build, deploy, and govern AI/ML models (including Generative AI) for banking use cases like pricing and anomaly detection, working across the full lifecycle from development to monitoring.
Build and deploy production-grade generative AI systems—LLMs, RAG, and AI agents—to automate document processing, customer support, and decision workflows in a regulated banking environment.
Build and deploy production-grade generative AI systems (LLMs, RAG, AI agents) for a large bank, focusing on NLP, document intelligence, and responsible AI in a regulated environment.
Build and deploy Palantir Foundry/AIP solutions for clients, turning data into AI-driven decision tools while collaborating with cross-functional teams and stakeholders.
Build and deploy AI-powered applications using cloud services and generative models, with a focus on production-grade ML pipelines and deep learning techniques.
Design and build enterprise-grade MLOps/LLMOps platforms on AWS SageMaker and EKS, automating model lifecycle from training to monitoring and ensuring scalable, secure AI deployments.
Lead a team building AI-ready data platforms and semantic layers for ecommerce analytics, machine learning, and generative AI at a global music company.
Build and deploy AI/ML systems for drug discovery and patient care, including GenAI, RAG, and agents, using Python, cloud platforms, and MLOps/LLMOps pipelines.
Senior Data Scientist builds and deploys ML and LLM-powered apps for healthcare clients, turning EHR, claims and clinical data into predictive models and AI workflows that improve patient outcomes and operations.
Staff Data Scientist leads data-driven decision-making for Ibotta’s core business domains, building and deploying ML models, optimizing pipelines, and mentoring teams to advance consumer behavior insights and business impact.
Lead design and delivery of Google Cloud Gen AI and agentic systems for healthcare clients, turning clinical data into safe, production-ready AI solutions that improve decision support and workflows.
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