Top Data & Analytics Jobs
As a Senior AI Engineer, you will design, implement, and maintain large-scale AI/ML pipelines, focusing on training and tuning deep learning models and evaluating large language models while working on data collection and feature engineering.
The Senior Director of Data will lead a multidisciplinary data organization focused on analytics, data infrastructure, and AI initiatives. Responsibilities include building data strategies, managing a team of data professionals, and ensuring effective use of customer data while partnering across the organization to optimize product features.
The Data Architect will lead the design and implementation of business data products, collaborate with various teams for data architecture and design, ensure compliance with data standards, and empower teams to enhance data quality and automation. The role also includes coaching and mentoring engineers while staying current with industry trends.
The Data Architect will lead the design and implementation of data products, collaborating with various product teams. Responsibilities include data modeling, ensuring adherence to data architecture guidelines, creating data quality standards, and managing a team of engineers. The position involves consulting on systems architecture and collaborating with Data Science teams.
As a Senior Machine Learning Engineer, you will design, develop, and deploy advanced computer vision models for applications like object detection and video analytics. You will optimize models for performance, mentor junior members, and collaborate with cross-functional teams to deliver scalable end-to-end solutions.
Develop and implement innovative pricing algorithms using machine learning and optimization techniques to solve complex pricing challenges. Engage in the entire ML development pipeline, from data acquisition to model deployment, while collaborating with cross-functional teams and effectively communicating results to diverse audiences.
The Senior Data Scientist will drive collaboration across teams to analyze data for fraud risks, build machine learning models, and provide insights for risk mitigation. Responsibilities include data refinement, project prioritization, mentoring, and establishing best practices in data science.
As a Machine Learning Engineer at Ibotta, you will design, develop, and deploy scalable machine learning models and infrastructure. You will collaborate with cross-functional teams, communicate complex solutions to stakeholders, and contribute to innovative projects involving recommendation systems and generative AI.
Featured Jobs
Senior Research Developer responsible for designing and implementing large-scale recommendation systems to personalize content recommendations for millions of anime fans worldwide. Key responsibilities include system architecture design, data preprocessing, model training, and operational excellence. Requires 5+ years of experience in recommendation systems, proficiency in Python, and familiarity with TensorFlow and PyTorch.
Senior Machine Learning Engineer responsible for designing and implementing recommendation systems to enhance user engagement and satisfaction through personalized content recommendations. Will work on system architecture, data preprocessing, model training, measurement feedback, and operational excellence. Requires 5+ years of experience in large-scale recommendation systems, proficiency in Python, familiarity with TensorFlow, PyTorch, GPU optimization, and cloud platforms.
The Senior Data Scientist will collaborate with a cross-functional team to design, develop, and deploy analytic models and high-impact data solutions in motorsports, leveraging machine learning and AI to support race teams. Responsibilities include analyzing and preprocessing diverse data, building predictive models, and presenting findings through visualization techniques.
Design and develop scalable and high-quality AI/ML features and solutions based on business needs. Collaborate with cross-functional teams to implement advanced AI technologies. Build infrastructure for AI/ML models and data collection mechanisms to meet customer demands. Ensure accuracy and freshness of enterprise operational data for thousands of customers.
The Deals Tech & Data Solutions Manager will lead teams in technology-driven solutions for mergers and acquisitions, leveraging data insights, and effectively managing client engagements. Responsibilities include supervising teams, maintaining quality deliverables, and communicating with both technical and business stakeholders.
As a Manager in the Machine Learning team, you will lead planning and coordination within sprints, build and maintain a machine learning platform, manage data pipelines, troubleshoot technical issues, and mentor team members.
The Senior Software Engineer will build a next-gen machine learning platform for advertising decisioning, develop scalable data analysis methods, collaborate with researchers, and advocate for best engineering practices.
The Lead Data Scientist will drive innovation in advertising by applying data science and statistical analysis. Responsibilities include developing models, defining key metrics, delivering insights, analyzing A/B tests, and mentoring team members within a collaborative environment.
As a Lead Machine Learning Engineer, you will design and implement ML solutions, optimize services, manage project milestones, communicate progress to stakeholders, and uphold best practices within the team while collaborating with engineers and product managers.
The Senior Data Engineer will design and develop batch and streaming data jobs, collaborate with ML engineers to automate data pipelines, and create tools to support event-driven architecture. The role requires working in an Agile environment and managing the lifecycle of data pipelines to support machine learning initiatives.
As a Senior Machine Learning Engineer at CrowdStrike, you will innovate machine learning technology for data science, create scalable microservices, build data pipelines, and collaborate across teams to enhance the cybersecurity platform. You'll engage in ongoing learning, maintain coding standards, and contribute to a collaborative environment while mentoring fellow engineers.
As a Machine Learning Engineer at Atlassian, you will develop and implement advanced machine learning algorithms, collaborating across business and engineering teams to build scalable recommendation systems. Your work will involve designing system architectures, conducting model experiments, and applying AI/ML to enhance business intelligence and growth.
As a Machine Learning Engineer, you will develop and implement machine learning and forecasting algorithms while collaborating with various teams. Your responsibilities include building scalable forecasting algorithms and managing the end-to-end ML development cycle to drive business impact.
As a Senior Machine Learning Engineer, you will develop and implement advanced machine learning algorithms focusing on forecasting. You'll collaborate with various teams to build scalable algorithms, empower model iteration, and provide guidance to junior engineers while ensuring the effective communication of model concepts to business partners.
As a Staff Data Scientist on the Dashboard Analytics team, you will leverage data engineering, business intelligence, and machine learning to drive data-driven decision-making and optimize seller experiences. You will collaborate with cross-functional teams, define key metrics, and communicate insights to inform strategic initiatives.
The AI/ML Scientist Manager will lead the deployment of AI solutions, ensuring best practices in model operationalization. Responsibilities include framework development, collaborative implementation with Data Scientists, team leadership of ML engineers, managing ML operations with ML Flow, and overseeing CI/CD pipelines.
The role involves leading and mentoring a team of ML engineers, developing frameworks for AI model deployment, assuring code quality through CI/CD processes, and collaborating with various stakeholders to transition AI PoCs into production applications. A strong emphasis is placed on operationalizing AI models and maintaining best practices in machine learning operations.
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