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The role involves developing cutting-edge Applied AI, infrastructure, and tooling to democratize Generative AI. Key tasks include building RAG pipelines, creating Python libraries for AWS, and implementing neural network architectures from research papers.
Design and scale backend services integrating Generative AI and Retrieval-Augmented Generation (RAG) for production. Develop AI agents and data pipelines to automate workflows and provide actionable insights.
Develop and refine predictive models and AI-driven insights to enhance intelligence and operational planning for a DoD customer. Implement Agile sprint cycles to integrate data from multiple sources and optimize analytic workflows.
The Data Scientist will lead the measurement science for the audio advertising platform, focusing on incrementality tests and marketing mix models. They will collaborate cross-functionally to quantify advertiser outcomes and influence product direction through causal evidence.
Design and deploy scalable AI, machine learning, and RAG solutions to drive business value within the Revenue Intelligence team. Build production-ready ML pipelines and AI-powered applications to transform enterprise data into actionable insights.
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Develop automated ELT pipelines in Snowflake and perform complex SQL transformations to monitor the charger network. Use data exploration, statistical methods, and visualization to identify and solve technical engineering problems.
Develop and refine predictive models and AI-driven insights to enhance intelligence and operational planning for a DoD customer. Implement Agile sprint cycles to integrate data from multiple sources and optimize analytic workflows.
The role involves partnering with Product and Engineering to design experiments and build ML/AI capabilities, including GenAI and recommendation systems. You will establish evaluation practices, monitor model quality, and coach other engineers on AI best practices.
Analyze and model end-user, application, and networking data to provide actionable insights and develop new product features. Implement GenAI techniques for capabilities such as summarization, classification, and intelligent assistants.
The role involves building and maintaining end-to-end machine learning models, LLMs, and agent systems to provide sustainability data. You will also analyze complex datasets and collaborate with stakeholders to align data products with business needs.
The role involves optimizing fraud detection models and policies using diverse data points on the Dynamic Decision Platform. It also requires scoping and delivering customer-focused analytical projects and presenting complex data patterns to non-technical audiences.
The role involves automating data collection processes and developing predictive models using various algorithms. The candidate will work within a dynamic team of data experts on various projects across Portugal.
Develop and implement innovative market data solutions by leveraging data assets to create strategic opportunities. Collaborate with customers, sales, and engineering teams to transform complex data into compelling narratives for stakeholders.
Train and iterate on custom ML models for customer feedback tasks including extraction, retrieval, and sentiment analysis. Build LLM-powered features and evaluation frameworks while collaborating with engineering to integrate models into production pipelines.
Build, validate, and deploy statistical and machine learning models including anomaly detection and generative AI. Collaborate with cross-functional teams to translate strategic business questions into scalable, production-ready data solutions.
Develop evaluation frameworks and metrics to ensure the safety and quality of autonomous vehicle performance and simulation. Collaborate with engineering and product teams to solve ambiguous data problems and facilitate deployment readiness.
Manage and analyze multi-source scientific datasets to improve operational efficiency for SEFSC survey programs. Develop and evaluate AI/ML models and analytical workflows to generate data products for scientific stakeholders.
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The role focuses on solving machine learning problems in NLP for enterprise clients and improving product suites. Primary duties include delivering production-ready code, building SQL queries for metrics, and prototyping data pipelines.
Extract and analyze large datasets from telemetry and test sources to derive actionable insights for agricultural machinery. Develop and refine machine learning models to optimize product performance and predict maintenance needs.
You will design and deploy data-driven machine learning models to prevent fraud and mitigate risk across various fintech use cases. Additionally, you will collaborate with engineering teams to scale these solutions into production while working directly with clients to address their unique security challenges.
Design and deploy advanced machine learning models to address critical financial use cases such as credit risk, fraud detection, and customer analytics. Collaborate with cross-functional teams to build scalable data pipelines and present actionable insights to senior stakeholders.
The Data Scientist will collaborate with cross-functional teams to design, build, and operationalize predictive models to solve business challenges. They are responsible for gathering and cleaning data, performing exploratory analysis, and communicating insights to stakeholders using visualization tools.
Design and build algorithms to power intelligence, analytics, and automated data labeling for the Reflow platform. Collaborate with engineering and product teams to productionize models and translate complex business problems into actionable data-driven solutions.
The Data Scientist will build predictive models and forecasting frameworks to analyze streaming data and audience behavior. They will also collaborate with cross-functional teams to optimize marketing campaigns and translate complex data into actionable business insights.
Design, train, and optimize machine learning and deep learning models for advertising and ranking systems. Collaborate with engineering and business teams to translate objectives into effective ML solutions while mentoring other data scientists.
The role involves working on AI and Machine Learning projects hosted in the Azure cloud, solving advanced ML challenges like forecasting and segmentation, and building solutions based on Large Language Models (LLMs). Responsibilities also include leading projects technically, preparing documentation, conducting training, and collaborating with clients to translate requirements into technical solutions.
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As a Data Scientist, you will work on client projects and internal research initiatives, solving real-world problems and contributing to AI alignment research. You will build data-driven solutions and leverage machine learning models to deliver impactful results.
Develop and deploy predictive models using machine learning and statistical techniques. Collaborate with cross-functional teams to identify opportunities for leveraging data and communicate results to stakeholders.
The Data Scientist will partner with various teams to investigate key business questions related to revenue growth and member behavior. They will develop member behavioral segmentations and translate complex analytical outputs into actionable insights for decision-makers.
The Senior Data Scientist will focus on search and be dedicated to the creation of next-generation AI and Machine Learning techniques for LexisNexis. This role involves solving challenging problems in natural language processing and deploying ethical generative AI solutions.
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