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Senior, Data Scientist - Machine Learning

Position Summary... What you'll do... We are looking for a Senior Machine Learning Engineer to lead the scaling and deployment of cutting-edge machine learning and deep learning models powering Data Ventures team. About The Team Data Ventures, akin to a nimble startup incubated within Walmart, is building the best-in-class suite of Data Products to deliver actionable, customer-centric insights and help merchants and suppliers make better business decisions on omni channel 360 performance. You will play a lead technical role in architecting scalable data science solutions working with business, product, and engineering partners and deliver outstanding business outcomes. You'll spend your days advocating and implementing MLOPs best practices and guiding other DS team members through the processes of optimizing and deploying various ML models and solutions. You will help establish best practices for model deployment, monitoring, and risk assessment, and be instrumental in productionizing and optimizing ML solutions for low latency inference times.What You'll Do:• The Senior Machine Learning Engineer is responsible for building scalable end-to-end data science solutions for our data products.• Works closely with data engineers, data analysts, data scientists and application developers to help build ML and statistics driven models and continuous model monitoring workflows.• Solve business problems by scaling advanced Machine Learning algorithms and complex statistical models on large volumes of data.• Own the MLOps lifecycle, from data monitoring to refactoring data science code to building robust ML model lifecycle.• Demonstrate strong thought-leadership and consult with product and business stakeholders to build, scale and deploy holistic machine learning solutions after successful prototyping.• Guide team members in achieving MLOps driven data science outcomes and handle multiple products and initiatives.• Follow industry best practices, stay up to date with and extend the state of the art in machine learning research and practice.• Participate in internal technical councils and represent the organization in forums that involve community of machine learning engineers.What You'll Bring:• You have engineering mindset and exposure to software engineering principles, Agile methodologies, CICD, distributed systems and implemented that in Machine Learning projects.• You have experience in NLP/NLU, Forecasting and Anomaly detection DS solution development and deployment in real world scenarios.• You have strong knowledge of Machine Learning, MLOps, MLflow, Kubeflow, Python/R, SQL, Big Data, GCP, Shell scripting.• Experience in scaling infrastructure to support high-throughput data-intensive applications using PySpark/GPU• You worked on integrating ML models with webservices.The above information has been designed to indicate the general nature and level of work performed in the role. It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to this job. The full Job Description can be made available as part of the hiring process.About Global TechImagine working in an environment where one line of code can make life easier for hundreds of millions of people and put a smile on their face. That's what we do at Walmart Global Tech. We're a team of 15,000+ software engineers, data scientists and service professionals within Walmart, the world's largest retailer, delivering innovations that improve how our customers shop and empower our 2.2 million associates. To others, innovation looks like an app, service, or some code, but Walmart has always been about people. People are why we innovate, and people power our innovations. Being human led is our true disruption.Benefits & PerksBeyond competitive pay, you can receive incentive awards for your performance. Other great perks include 401(k) match, stock purchase plan, paid maternity and parental leave, PTO, multiple health plans, and much more.Equal Opportunity EmployerWalmart, Inc. is an Equal Opportunity Employer - By Choice. We believe we are best equipped to help our associates, customers, and the communities we serve live better when we really know them. That means understanding, respecting, and valuing diversity- unique styles, experiences, identities, ideas, and opinions - while being inclusive of all people.At Walmart, we offer competitive pay as well as performance-based bonus awards and other great benefits for a happier mind, body, and wallet. Health benefits include medical, vision and dental coverage. Financial benefits include 401(k), stock purchase and company-paid life insurance. Paid time off benefits include PTO (including sick leave), parental leave, family care leave, bereavement, jury duty, and voting. Other benefits include short-term and long-term disability, company discounts, Military Leave Pay, adoption and surrogacy expense reimbursement, and more.You will also receive PTO and/or PPTO that can be used for vacation, sick leave, holidays, or other purposes. The amount you receive depends on your job classification and length of employment. It will meet or exceed the requirements of paid sick leave laws, where applicable.For information about PTO, see https://one.walmart.com/notices .Live Better U is a Walmart-paid education benefit program for full-time and part-time associates in Walmart and Sam's Club facilities. Programs range from high school completion to bachelor's degrees, including English Language Learning and short-form certificates. Tuition, books, and fees are completely paid for by Walmart.Eligibility requirements apply to some benefits and may depend on your job classification and length of employment. Benefits are subject to change and may be subject to a specific plan or program terms.For information about benefits and eligibility, see One.Walmart .Sunnyvale, California US-11349:The annual salary range for this position is $117,000.00-$234,000.00Bentonville, Arkansas US-10735:The annual salary range for this position is $90,000.00-$180,000.00Additional compensation includes annual or quarterly performance bonuses.Additional compensation for certain positions may also include:- StockMinimum Qualifications...Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.Option 1- Bachelor's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 3 years' experience in an analytics related field. Option 2- Master's degree in Statistics, Economics, Analytics, Mathematics, Computer Science, Information Technology, or related field and 1 years' experience in an analytics related field. Option 3 - 5 years' experience in an analytics or related field.Preferred Qualifications...Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch)Primary Location...2501 Se J St, Ste A, Bentonville, AR 72716-3724, United States of America
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What You Should Know About Senior, Data Scientist - Machine Learning, Walmart

At Walmart, we're on the lookout for a Senior Data Scientist specializing in Machine Learning to join our innovative Data Ventures team in Cassville, MO. As a pivotal member of our squad, you'll get to lead the scaling and deployment of state-of-the-art machine learning models that push boundaries and enhance customer experiences. Picture this: you’ll be collaborating closely with our talented data engineers, analysts, and product developers to craft tailored ML solutions that tackle real-world business challenges. You'll also bring your expertise to the table by advocating for MLOps best practices, allowing your peers to thrive in achieving optimal deployment and monitoring of various models. Your creativity and technical skills will shine as you establish industry best practices, ensuring our solutions are not only robust but also efficient. You’ll guide and mentor team members and dive deep into the world of NLP, anomaly detection, and forecasting. Being a part of Walmart means working for a company that truly values diversity, innovation, and a customer-first approach. You will thrive in a space where your insights and coding prowess can impact millions and make a difference every day. So if you’re ready to step into a role that offers immense growth, does good work, and celebrates outstanding achievements, apply now and join us in continuing to change the way customers shop!

Frequently Asked Questions (FAQs) for Senior, Data Scientist - Machine Learning Role at Walmart
What are the responsibilities of a Senior Data Scientist - Machine Learning at Walmart?

As a Senior Data Scientist - Machine Learning at Walmart, you'll be responsible for architecting and deploying scalable data science solutions. This role involves collaborating with various stakeholders, including data engineers and application developers, to build ML-driven models. You will also focus on optimizing the MLOps lifecycle, guiding team members in implementing best practices, and addressing complex business problems with your data expertise.

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What qualifications are required for the Senior Data Scientist - Machine Learning position at Walmart?

To qualify for the Senior Data Scientist - Machine Learning position at Walmart, candidates should have a Bachelor’s degree in a relevant field such as Statistics or Computer Science, accompanied by three years of experience in analytics. Alternatively, a Master’s degree with one year of relevant experience or five years in an analytics role is also acceptable. Proficiency in Python, machine learning principles, and experience with MLOps tools like MLflow is essential.

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What skills are important for a successful Senior Data Scientist - Machine Learning at Walmart?

Successful Senior Data Scientists - Machine Learning at Walmart should possess strong skills in machine learning frameworks, data scaling, and statistical modeling. Experience in NLP, anomaly detection, and familiarity with tools like PySpark, Kubernetes, and cloud platforms such as GCP are vital. Additionally, a mindset focused on engineering best practices and Agile methodologies is highly advantageous.

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How does Walmart ensure a positive work environment for its Senior Data Scientist - Machine Learning?

Walmart promotes a positive work environment for its Senior Data Scientist - Machine Learning by emphasizing diversity, inclusion, and ongoing learning. Employees are encouraged to share insights and participate in community forums, ensuring everybody has a voice. The company also provides robust benefits, including tuition assistance, health coverage, and performance-based bonuses, helping employees maintain a balance between work and personal life.

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What are the growth opportunities for a Senior Data Scientist - Machine Learning at Walmart?

At Walmart, a Senior Data Scientist - Machine Learning has numerous growth opportunities, from advancing technical skills to exploring leadership roles. The fast-paced environment includes participation in internal councils, exposure to cutting-edge research, and a chance to work on impactful projects that affect millions. With ongoing development programs and a supportive culture, you will have every chance to ascend in your career.

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Common Interview Questions for Senior, Data Scientist - Machine Learning
How do you approach building scalable machine learning models?

When addressing scalability in machine learning models, I first ensure that the data pipeline is robust and can handle high volumes of data efficiently. I typically leverage distributed computing frameworks such as Apache Spark to process large datasets and utilize cloud-based solutions for scalability. I also prioritize training models iteratively and focus on deploying solutions that can dynamically adapt to changing data patterns.

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Can you explain your experience with MLOps and its importance?

MLOps has been a core aspect of my work. I believe its importance lies in streamlining the model lifecycle, from development through deployment and monitoring. I've implemented practices that ensure constant feedback loops between data scientists and operations, resulting in faster iterations and improved model performance over time. Establishing automation for testing and integration is key to achieving operational efficiency.

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What machine learning frameworks and libraries are you most proficient in?

I am proficient in several machine learning frameworks, including TensorFlow, PyTorch, and scikit-learn, which I have utilized to build various ML models. Additionally, I have experience using MLflow for managing the machine learning lifecycle and Kubeflow for deploying machine learning workflows in Kubernetes environments, allowing for continuous integration and delivery of models.

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How do you handle unexpected results in your models?

When faced with unexpected results, I first validate my data inputs and ensure that they are correct and well-prepared. From there, I perform error analysis to see which elements contributed to the discrepancy. Adjusting model parameters or revisiting feature selection are common steps I take. I also emphasize documentation and sharing insights with team members to fine-tune the approach collectively.

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Describe a project where you implemented deep learning. What challenges did you face?

In one project, I developed a deep learning model for image classification using convolutional neural networks. The main challenge was obtaining a sufficiently large and diverse dataset for training. To overcome this, I employed data augmentation techniques to increase the dataset and improve model generalization. The project was a fantastic learning opportunity as it taught me the intricacies of hyperparameter tuning.

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

I regularly read research papers from platforms like arXiv and attend relevant conferences and workshops to keep up with advancements in machine learning. Additionally, I participate in online forums and communities, where I can discuss new methods and tools with peers. This proactive approach helps me stay current and apply best practices and cutting-edge techniques to my projects.

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What is your experience with cloud platforms and how do they impact machine learning projects?

I have direct experience using cloud platforms like Google Cloud Platform and AWS for deploying machine learning models. These platforms provide immense scalability and computational power, which is critical for training complex models on large datasets. Moreover, their managed services for data storage and processing significantly simplify the deployment and maintenance of machine learning projects.

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Explain how you would present complex machine learning concepts to a non-technical audience.

When presenting complex ML concepts to a non-technical audience, I focus on clarity and relevance. I use relatable analogies and visuals to illustrate key points, breaking down concepts into simple terms. Additionally, I tie my presentation back to business objectives and outcomes to demonstrate the value of the work, making it easier for stakeholders to grasp the ideas without delving into technical jargon.

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How do you prioritize your workload when managing multiple ML projects?

To prioritize my workload effectively, I first assess project timelines, stakeholder expectations, and overall impact. I utilize project management tools to track progress and deadlines, allowing me to allocate resources accordingly. Regular check-ins with my team ensure alignment of priorities and make it easy to adapt as project needs evolve.

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What role does data preprocessing play in your workflows?

Data preprocessing plays a crucial role in my workflows and serves as the foundation for accurate modeling. I rigorously clean the data, handle missing values appropriately, and ensure features are uniformly scaled or normalized. This step is critical, as it enhances the model's performance and reduces errors, ultimately leading to more reliable predictions.

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Full-time, on-site
DATE POSTED
December 12, 2024

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