Top Data & Analytics Jobs
As a Research Scientist in NLP at Kensho, you will develop and deploy innovative AI solutions, focusing on areas such as long-context question-answering and alignment techniques. You'll collaborate with a team of experienced scientists to fuel innovation and publish research, while leveraging substantial computational resources for model development.
As a Senior Research Scientist at Kensho, you will work on cutting-edge Machine Learning and Natural Language Processing projects, such as long-context document QA, complex reasoning, and developing Large Language Models. You will contribute to creating evaluation benchmarks and improving alignment techniques while collaborating with academia and leveraging access to high-performance GPUs.
The Senior Software Engineer will develop and implement machine learning models, collaborate with teams to deploy AI solutions, maintain ML pipelines, and stay updated with advancements in AI. Responsibilities encompass AI application development across Disney's segments.
The Principal Data Scientist will lead analytical initiatives and provide strategic recommendations to solve business problems using advanced analytics, machine learning, and statistical methodologies. Responsibilities include collaborating with cross-functional teams, developing analytical frameworks, and delivering insights to leadership through presentations and reports.
In this role, you'll lead analytical initiatives by employing techniques like machine learning and optimization to address business problems. Responsibilities include managing risks, developing reports and dashboards, collaborating across departments, and delivering presentations on findings to various leadership levels.
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The Senior Machine Learning Engineer will develop and optimize NLP algorithms, train large language models, collaborate with cross-functional teams, and mentor junior engineers, all while advancing the company's technology stack and delivering new features rapidly.
The Chief Scientist for Edge and Spectral AI at BAE Systems FAST Labs will lead research initiatives in advanced technologies like RF, ML, and cognitive warfare. Responsibilities include technical leadership, business development, and collaboration with scientific teams to tackle defense challenges and transition innovations into BAE Systems products.
The Senior Manager, Data Science will lead and grow a team of skilled data scientists, partner with senior leaders to identify business opportunities, and build KPIs. This role involves driving analytics strategy, managing roadmapping and sprint planning, and delivering high-quality analytic insights and data-driven solutions.
Featured Jobs
As a Staff Machine Learning Engineer, you'll lead the development of AI solutions, working with application teams and data scientists to build a reliable AI infrastructure at Robinhood. You'll oversee high-impact projects for a centralized AI platform, ensure seamless integration, and drive the implementation of effective ML models and data access strategies.
As a Senior Machine Learning Engineer, you will lead the development and deployment of machine learning models to assess business risk. You'll transform diverse data sources into meaningful insights, collaborating with Product Management and Software Engineers during bi-weekly sprints.
The Machine Learning Engineer at Kalepa will frame, develop, and deploy machine learning models to assess business risks. This role involves analyzing structured and unstructured data to generate insights, collaborating closely with product and software teams, and working autonomously on projects within two-week sprints.
As a Machine Learning Engineer at Kalepa, you will lead the development and deployment of machine learning models, transforming structured and unstructured data into insights about business risks. The role emphasizes ownership over projects, collaboration with product management, and software engineers in an agile environment.
As a Machine Learning Engineer at Kalepa, you will lead the development and deployment of machine learning models that analyze the risk of various business classes. You'll transform vast structured and unstructured datasets into valuable insights while collaborating with product management and software engineers in an agile environment.
As a Machine Learning Engineer at Curai Health, you will develop and enhance AI systems for clinician and patient applications. This includes fine-tuning large language models, designing evaluations, performing data analysis, and collaborating with diverse teams to drive innovation in ML technologies.
As a Senior ML Engineer at Curai Health, you will develop and enhance AI systems for patient and physician applications. You will fine-tune large language models, perform model evaluations, mentor team members, and collaborate across product, engineering, and clinical teams to generate impactful machine learning solutions.
As a Machine Learning Engineer, you will frame, develop, and deploy machine learning models to assess risk for various businesses using structured and unstructured data. The role emphasizes ownership of projects and collaboration with product management and software engineers in a sprint-based environment.
The Senior Machine Learning Engineer will lead the development and deployment of machine learning models to analyze risks for various businesses, utilizing both structured and unstructured data. The role requires close collaboration with Product Management and Software Engineers in an agile environment, with a focus on delivering actionable insights.
The Machine Learning Engineer will lead the development and deployment of machine learning models to assess risks associated with various businesses. Responsibilities include converting structured and unstructured data into actionable insights and working closely with product management and software engineers in a sprint-based environment.
The Senior Machine Learning Engineer will lead the framing, development, and deployment of machine learning models at scale, focusing on understanding the risk of various classes of businesses. They will turn structured and unstructured data into actionable insights, working collaboratively in sprints with Product Management and Software Engineers.
As a Senior Machine Learning Engineer, you will lead the development and deployment of machine learning models to assess business risks. Responsibilities include processing large datasets and collaborating with product and software teams to drive project direction.
As a Senior Applied Scientist at Chewy, you'll leverage machine learning and data science to enhance product discovery and optimize advertising solutions. Responsibilities include deploying models, collaborating with product and engineering teams, leading model development, publishing research, mentoring junior scientists, and communicating insights to leadership.
As an Applied Scientist at Chewy, you will develop machine learning and data science solutions to enhance the shopping experience for pet owners. Responsibilities include improving product relevance, click-through prediction models, and auction algorithms, while collaborating with product and engineering teams. You will also publish research and mentor junior scientists.
The Senior Lead Machine Learning Scientist will develop and implement machine learning algorithms, focusing on deep learning for multimodal data problems. This role involves leading research projects, collaborating with cross-functional teams, and staying current with advancements in AI and machine learning.
The Director of Data Science & Analytics will lead a team to drive strategic insights through data analytics in the Amazon marketplace. Responsibilities include leveraging technology for innovative solutions, optimizing reporting tools, conducting market research, fostering a data-driven culture, and ensuring compliance and data integrity. This role involves collaboration across multiple teams to identify new opportunities and improve business strategies.
The Machine Learning Engineer will frame, develop, and deploy machine learning models at scale, analyzing structured and unstructured data to derive insights about business risks. Responsibilities include collaborating with Product Management and Software Engineers within a two-week sprint framework, while maintaining full project ownership.
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