Applied Data Researcher who combines scientific depth with a proactive builder mindset, working with a petabyte-scale dataset to tackle problems in AI product development.
Locations (2)
San Francisco, California, United States
New York, New York, United States
Compensation
Annual Base Salary Range:
$235,000—$376,000 USD
Responsibilities
Identify and vet consequential opportunities across Data products and tools, AI measurement and evaluation, and applied data capabilities
Develop and validate methods for evaluating generative AI and agentic workflows
Personally write code and build or adapt LLM-powered tools, agentic workflows, models, prototypes, and demos
Provide technical direction and oversight for larger projects, delegate work to teams of senior and staff scientists
Independently drive delivery or partner with engineering to turn useful prototypes into maintained systems
Requirements
MS or PhD in machine learning, computer science, statistics, or a similar quantitative discipline
Develop and apply modern machine learning and AI across a range of products and business problems in regulated financial services.
Compensation
Base Compensation Range: $132,000 - $198,000/yr
25% annual bonus target, paid quarterly
Company equity in the form of RSUs
Responsibilities
Identify high-impact opportunities for machine learning and AI in partnership with product, engineering, operations, and business teams
Design, train, fine-tune, and evaluate machine learning and deep-learning models
Work with foundation models across language, vision, and multimodal applications
Develop robust datasets, features, training pipelines, and evaluation frameworks
Run efficient experiments on GPU infrastructure and improve model quality, speed, and cost
Requirements
Bachelor’s or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience
Nice to have: Master’s degree or PhD in Computer Science, Machine Learning, or a related field
Senior Applied Scientist, Property Analytics to help reinvent real estate lending by tackling complex research and machine learning problems related to automated valuation models (AVMs), asset valuation, and process optimization.
Compensation
Base Compensation Range: $132,000 - $198,000/yr
25% annual bonus target, paid quarterly
Company equity in the form of RSUs
Responsibilities
Lead the end-to-end design, architecture, development, and deployment of state-of-the-art automated valuation modeling (AVM) systems for novel lending products
Architect and refine innovative machine learning, deep learning, and statistical models for asset valuation, incorporating spatial-temporal trends, macroeconomic factors, market liquidity, and property risk dynamics
Establish robust quantitative frameworks for continuous model performance assessment, monitoring, and automated retraining across changing real estate cycles
Build analytical tools and frameworks to empower cross-functional teams across risk, underwriting, and capital markets
Partner closely with data infrastructure and MLOps engineering teams to drive initiatives that enhance data capabilities, build feature stores, and streamline real-time inference infrastructure
Requirements
Ph.D. or Master’s degree in a quantitative discipline (Computer Science, Data Science, Statistics, Economics, Applied Mathematics, or Machine Learning)
Help enterprise medical device, health technology, and diagnostics customers solve real problems by selling AWS cloud solutions and driving revenue growth.
Locations (3)
Detroit, Michigan, United States
Boston, Massachusetts, United States
Chicago, Illinois, United States
Compensation
The base salary range for this position is listed below. Your Amazon package will include sign-on payments, restricted stock units (RSUs), and sales incentives. Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, IL, Chicago - 142,800.00 - 193,200.00 USD annually
USA, MA, Boston - 142,800.00 - 193,200.00 USD annually
USA, MI, Detroit - 142,800.00 - 193,200.00 USD annually
Responsibilities
Applied Scientist II, AWS Applied Data Science Labs
Apply the latest research, state of the art machine learning and GenAI to build highly scalable models in support of predictive research and products used across AWS Sales and leadership.
Compensation
USA, WA, Seattle - 142,800.00 - 193,200.00 USD annually
Responsibilities
Design and execute science experiments in short and agile cycles
Work closely with business leaders to identify opportunities to drive business growth by applying machine learning and building out predictive models to support analytics and insights
Mentor junior scientists
Contribute to peer reviews
Requirements
PhD in computer science, machine learning, engineering, or related fields
Nice to have: Professional software development
Nice to have: Solving business problems through machine learning, data mining and statistical algorithms
Develop highly scalable algorithms based on state-of-the-art machine learning and neural network methodologies; design, research, and implement new AI techniques; adapt ML methods to modern parallel environments; gather data for training; train ranking models; conduct experiments; improve Meta's platforms; present economic and game-theoretic analyses; develop scalable classifiers and tools.
Locations (4)
Redmond, Washington, United States
Burlingame, California, United States
New York, New York, United States
Seattle, Washington, United States
Responsibilities
Develop highly scalable algorithms based on state-of-the-art machine learning and neural network methodologies
Experienced Data Scientist to join remotely for a long-term mission supporting a European Institution based in Brussels, focusing on developing and implementing machine learning algorithms and statistical models.
Responsibilities
Developing and implementing machine learning algorithms, statistical models, and scripts to solve specific business problems
Conducting rigorous experiment design (e.g., A/B testing), employing cross-validation techniques, and performing statistical significance testing to validate findings and model performance
Elaborating processes to ensure the compliant implementation of regulatory frameworks (e.g., EU AI Act), including model risk management, monitoring for drift/bias, and comprehensive documentation (e.g., model cards)
Data collection, transformation and integration: Analysing the needs and identify suitable data sources and flows. Organising the execution of the data collection and integration processes, verifying results, proposing improvements and implementing them in cooperation with the IT department. Organising the related user acceptance tests.
Data validation, visualisation and publication: Organising the data validation and the data quality checks, including through automated processes. Analyzing needs and creating reports, data visualisations and publications, in cooperation with the IT department.
Develop innovative deep learning and machine learning approaches to address real-world challenges at the intersection of artificial intelligence, agriculture, weather, and climate.
Responsibilities
Conduct independent research in deep learning, machine learning, and predictive modeling for agriculture, weather, and climate applications
Develop forecasting models across multiple prediction horizons, ranging from days to months
Explore advanced deep learning architectures, including transformers, diffusion, spatiotemporal models, and other state-of-the-art approaches
Develop robust, and trustworthy AI models, emphasizing uncertainty quantification, interpretability, and predictive reliability to support informed decision-making
Collaborate with multidisciplinary teams to translate research findings into operational forecasting solutions
Build models that learn from real-world user interactions and own the training, evaluation, and reinforcement learning systems powering AI personal assistants for millions of Indians.
Responsibilities
Build and improve models that learn from real-world consumer interactions and task outcomes
Own the post-training pipeline for open-weight LLMs using SFT, DPO, and reinforcement learning techniques
Design training systems that use agent trajectories, user preferences, and task outcomes as training signals
Build and maintain datasets by collecting, filtering, labelling, and deduplicating trajectory and preference data
Define what constitutes a successful task outcome and translate it into measurable training rewards
health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
Health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
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