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Sr. Machine Learning Scientist, OTS DataTech AI

DescriptionAt OpsTech Solutions (OTS), we are a technology centric services organization that designs, builds, and sustains the invisible, high-quality network, compute infrastructure and device scaffolding that empowers and protects Amazon’s global Operations.The OTS DataTech team drives enterprise data strategy and support across OTS. Our charter encompasses OTS-wide efforts, including Data as a Product (DaaP), enterprise data infrastructure, AI/ML capability, and supporting specific business-critical programs, fueling innovation and automation for OTS.We are looking for a passionate, talented, innovative, experienced and Senior Machine Learning Scientist with a background in building cutting-edge scientific and engineering components that are highly scalable, extensible, and robust to enable exponential growth and adoption of AI/ML within OTS. In this role, you will play a pivotal role in shaping the vision, roadmap, and execution of science and engineering-based solutions from beginning to end.You will build foundational GenAI components that will enable our customers to build GenAI applications for their use cases across OTS. You will enable the seamless integration of scientific products with new and existing systems, ultimately leading to increased operational efficiency and productivity across OTS. You will also work on projects involving supervised and unsupervised learning, NLP, and more. You will be responsible to build and maintain an MLOps Platform that will support end-to-end scientific operations for a wide range of AI/ML use cases within the realms of GenAI, supervised and unsupervised learning, optimization, and more.You will evangelize the adoption of our scientific solutions across the organization. You will be closely partnering with a cross-functional team of stakeholders including with Applied Scientists, Data Scientists, Data Engineers, Product Managers, and Technical Program Managers.As part of other initiatives, you will also contribute to building a data infrastructure that supports our DataMesh framework, enabling engineering and BI self-service architecture for DaaP, 3P software integrations, and more.Come join OTS DataTech as we continue to innovate and pioneer the AI/ML space within OTS!Key job responsibilities• Build and maintain an MLOps Platform that supports end-to-end AI/ML operations.• Shape the vision and roadmap for AI/ML across the organization, leading their research and development from concept to deployment from an engineering perspective.• Guide teams to adopt software engineering best practices that uplift our Operational Excellence standards.• Promote and facilitate the adoption of AI/ML solutions across the organization.• Build and maintain data infrastructure that supports the DataMesh framework and enables self-service architecture.A day in the lifeAmazon offers a full range of benefits that support you and eligible family members, including domestic partners and their children. Benefits can vary by location, the number of regularly scheduled hours you work, length of employment, and job status such as seasonal or temporary employment.The benefits that generally apply to regular, full-time employees include:• Medical, Dental, and Vision Coverage• Maternity and Parental Leave Options• Paid Time Off (PTO)• 401(k) PlanIf you are not sure that every qualification on the list above describes you exactly, we'd still love to hear from you!At Amazon, we value people with unique backgrounds, experiences, and skillsets. If you’re passionate about this role and want to make an impact on a global scale, please apply!Basic Qualifications• PhD in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field, or Master's degree and 4+ years of industry or academic research experience• 5+ years of applied research experience• 5+ years of building machine learning models or developing algorithms for business application experience• 5+ years of industry or academic research experience• Experience programming in Java, C++, Python or related language• Experience with large scale machine learning systems such as profiling and debugging and understanding of system performance and scalability• Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.Preferred Qualifications• 5+ years of experience in full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations• Experience developing, building and implementing complex software systems and machine learning systems that have been successfully delivered to customers.• Experience developing, building, and implementing data engineering pipelines and infrastructure.• Experience with AWS technologies.• Experience with MLOps tools and frameworks (e.g., SageMaker, MLflow).• Background in AI/ML, including GenAI, supervised and unsupervised learning, and optimization algorithms.• Experience with ML frameworks (e.g., PyTorch, TensorFlow) and application development frameworks (e.g., LangChain).• Publications at top-tier peer-reviewed conferences or journals.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.Company - Amazon.com LLCJob ID: A2839816

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What You Should Know About Sr. Machine Learning Scientist, OTS DataTech AI, Amazon

At OpsTech Solutions (OTS), we're not just a tech services organization; we're innovators shaping the future of operations. Our Bellevue, WA team is on the lookout for a passionate and talented Sr. Machine Learning Scientist ready to dive into the heart of AI and ML. You’ll play a pivotal role in driving our enterprise data strategy and support while working with cutting-edge technology in a stimulating environment. In this role, you'll not only build foundational GenAI components but also evangelize our scientific solutions across the organization. Your efforts in creating an MLOps Platform will help streamline AI and ML operations from concept to deployment. You'll be collaborating with an awesome cross-functional team of Applied Scientists, Data Engineers, and Product Managers. We need your expertise in NLP, supervised and unsupervised learning, and data infrastructure to elevate our operational excellence. If you’re eager to lead the transformation of AI/ML within OTS and make a significant impact, come join us. Together, we can innovate and pioneer the AI/ML space like never before!

Frequently Asked Questions (FAQs) for Sr. Machine Learning Scientist, OTS DataTech AI Role at Amazon
What are the responsibilities of a Sr. Machine Learning Scientist at OTS DataTech?

The Sr. Machine Learning Scientist at OTS DataTech will be responsible for building and maintaining an MLOps Platform to support comprehensive AI/ML operations. Other duties include shaping the organization’s AI/ML vision, facilitating the adoption of machine learning solutions, collaborating with cross-functional teams, and contributing to the development of a robust data infrastructure.

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What qualifications are needed for the Sr. Machine Learning Scientist position at OTS DataTech?

Candidates applying for the Sr. Machine Learning Scientist role at OTS DataTech should hold a PhD in a quantitative field like engineering or computer science, or a Master’s degree with relevant experience. Additionally, a background that includes applied research, machine learning model development, proficiency in programming languages such as Python or Java, and experience with large-scale systems is essential.

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How does OTS DataTech support career growth for Sr. Machine Learning Scientists?

At OTS DataTech, we encourage continuous learning and growth. As a Sr. Machine Learning Scientist, you'll have access to innovative projects, collaboration with industry leaders, and opportunities to influence the AI/ML strategies across the organization. Our supportive environment values creativity and allows you to shape the future of AI in operations.

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What technical skills are important for a Sr. Machine Learning Scientist at OTS DataTech?

Important technical skills for a Sr. Machine Learning Scientist at OTS DataTech include experience with machine learning frameworks like TensorFlow and PyTorch, familiarity with MLOps tools such as SageMaker, and proficiency in programming languages like Python, Java, or C++. Additionally, expertise in building data engineering pipelines and understanding system performance is crucial.

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What is the work culture like at OTS DataTech for a Sr. Machine Learning Scientist?

The work culture at OTS DataTech is collaborative, innovative, and driven by a passion for technology. As a Sr. Machine Learning Scientist, you’ll work with diverse teams, embrace challenges, and contribute to exciting projects that make a real-world impact. We promote a flexible and inclusive environment where everyone’s ideas are valued.

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Common Interview Questions for Sr. Machine Learning Scientist, OTS DataTech AI
Can you explain your experience with building machine learning models?

In my previous roles, I have developed various machine learning models ranging from predictive analytics to NLP applications. I emphasize understanding the business problem before selecting the right algorithms and frameworks, ensuring that the solutions are not only accurate but also scalable and maintainable.

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How do you handle data quality issues before building a model?

I believe that ensuring high data quality is paramount for success in machine learning projects. I usually start by conducting thorough data exploration, identifying inconsistencies, and applying techniques like data cleaning, normalization, or imputation to prepare the dataset effectively for modeling.

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What is your approach to selecting the right ML algorithm for a problem?

Selecting the right algorithm begins with a clear understanding of the problem statement, the nature of the data, and whether the task is supervised or unsupervised. I evaluate multiple algorithms based on performance metrics such as accuracy, precision, and robustness and run experiments to compare results before deciding.

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Describe a challenging machine learning project you've worked on.

In a recent project, I faced challenges with data scarcity and noise in the dataset. By leveraging advanced augmentation techniques and tuning the model iteratively, I was able to develop an effective solution that improved our classification accuracy significantly, demonstrating the importance of innovation amidst constraints.

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What tools and frameworks do you prefer for MLOps?

I prefer using MLOps tools like AWS SageMaker and MLflow due to their robustness and the ability to streamline the deployment process. These tools facilitate collaboration across teams and allow easy tracking of model performance and versioning, ensuring efficient management of the ML lifecycle.

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How do you ensure your models are scalable?

Scalability is a priority in my workflows. I design architectures that can handle large datasets and high query loads from the onset. This often involves containerizing applications, using cloud services for storage and compute, and optimizing the algorithms to ensure they perform well under various loads.

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How do you fine-tune your models?

Fine-tuning is done by employing techniques like grid search and cross-validation to find the best hyperparameters. I also analyze the model's performance metrics to identify areas for improvement, allowing me to systematically enhance accuracy and reduce overfitting while validating results thoroughly.

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Can you explain your experience with Natural Language Processing?

My experience with NLP includes developing sentiment analysis models and chatbots. I have utilized libraries like NLTK and SpaCy to preprocess text data and implemented algorithms that incorporate both traditional statistical methods and deep learning to extract insights efficiently.

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How do you stay updated with the latest in machine learning?

To stay updated, I regularly follow top tier conferences and journals in AI/ML, participate in online courses, and engage with the community through meetups and forums. This holistic approach allows me to incorporate the latest research advancements into my work.

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How important is teamwork in your approach to ML projects?

Teamwork is crucial in my approach. I believe that collaboration fosters creativity and enhances problem-solving. I actively engage with cross-functional teams to leverage diverse expertise, ensuring that we develop comprehensive solutions that meet business needs effectively.

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Amazon is guided by four principles: customer obsession rather than competitor focus, passion for invention, commitment to operational excellence, and long-term thinking.

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CULTURE VALUES
Inclusive & Diverse
Rise from Within
Mission Driven
Diversity of Opinions
Work/Life Harmony
Transparent & Candid
Growth & Learning
Fast-Paced
Collaboration over Competition
Take Risks
Friends Outside of Work
Passion for Exploration
Customer-Centric
Reward & Recognition
Feedback Forward
Rapid Growth
BENEFITS & PERKS
Medical Insurance
Paid Time-Off
Maternity Leave
Mental Health Resources
Equity
Paternity Leave
Fully Distributed
Flex-Friendly
Some Meals Provided
Snacks
Social Gatherings
Pet Friendly
Company Retreats
Dental Insurance
Life insurance
Health Savings Account (HSA)
FUNDING
DEPARTMENTS
SENIORITY LEVEL REQUIREMENT
INDUSTRY
TEAM SIZE
EMPLOYMENT TYPE
Full-time, on-site
DATE POSTED
December 2, 2024

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