New opportunity

Data Scientist 4

Lam Research · Bengaluru, India

About this role

The Office of the CTO at Lam Research is seeking a Data Scientist 4 to drive innovation in AI and machine learning. This role focuses on building scalable AI/ML and Generative AI solutions to support critical business needs and enhance decision-making processes.

Responsibilities

Design, develop, and deploy end-to-end AI/ML and Generative AI solutions.
Build scalable machine learning architectures and evaluate modern tools and frameworks.
Develop production-ready models on cloud environments (preferably Azure) using MLOps practices.
Partner with cross-functional teams to translate business problems into technical solutions.
Lead technical design discussions and guide teams through complex AI/ML challenges.
Establish monitoring, governance, and fail-safe mechanisms for model reliability.
Deliver rapid POCs and extract insights from complex datasets.

Required Qualifications

8-12 years of experience delivering enterprise-scale data science and AI/ML solutions.
Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Engineering, or a related field.
Strong programming skills in Python or R and expertise with ML/DL frameworks.
Experience with cloud platforms (Azure, AWS, or GCP) and big-data technologies (Hadoop, Spark).
Proven understanding of MLOps, model deployment, and ML fail-safe principles.
Hands-on experience leading projects using agile methodologies.
Strong communication and stakeholder management skills.

Preferred Qualifications

Experience designing large-scale AI/ML or GenAI architectures.
Background in building highly scalable production ML systems.
Familiarity with advanced statistical modelling and applied machine learning techniques.
Experience guiding teams in high-impact technical environments.

Work Arrangement:

This is a hybrid role under the 'On-site Flex' model, requiring 3+ days per week on-site.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonRMachine LearningDeep LearningGenerative AIMLOpsAzureAWSGCPHadoopSparkStatistical ModellingAgile MethodologiesStakeholder Management

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

End-to-End AI/ML and GenAI Deployment
The role requires designing, developing, and deploying scalable AI solutions from concept to production. You must demonstrate the ability to move beyond POCs to deliver production-ready models that address critical business needs.
MLOps and Model Governance
A core priority is establishing robust MLOps practices, including monitoring, governance, and fail-safe mechanisms. You will be expected to ensure model reliability and continuous improvement within cloud environments, specifically Azure.
Cross-Functional Stakeholder Management
You must translate complex business problems into technical AI/ML solutions. Success depends on your ability to partner with data scientists, engineers, and business leaders to drive measurable impact.
Technical Leadership and Mentorship
As a senior-level contributor, you are expected to lead design discussions and guide teams through complex technical challenges, fostering an innovation-driven culture within the Office of the CTO.

Likely challenges to think through

Balancing Innovation with Production Reliability
The challenge lies in delivering rapid POCs while simultaneously building scalable, production-grade architectures. Prepare by discussing how you manage technical debt and maintain rigorous MLOps standards during fast-paced development cycles.
Translating Ambiguous Business Needs
Bridging the gap between high-level business objectives and technical implementation is difficult. Prepare by providing examples of how you have elicited requirements from non-technical stakeholders to define clear, measurable project goals.
Navigating Complex Enterprise Data Ecosystems
Working with diverse datasets in a large-scale environment like Lam Research requires expertise in big-data technologies (Hadoop, Spark). Prepare to discuss how you handle data quality, integration, and governance in high-impact environments.

Practice your answers

Technical practice

Designing Scalable AI/ML Architectures

What to coverDiscuss your approach to designing systems that handle large-scale enterprise data. Focus on how you ensure modularity, scalability, and the selection of appropriate frameworks for high-throughput environments.

Implementing MLOps for Production Reliability

What to coverExplain your strategy for maintaining production-ready models. Address the full lifecycle, including CI/CD for ML, automated testing, and the specific MLOps tools you have used to ensure model stability.

Generative AI Integration Strategies

What to coverDescribe how you evaluate and deploy Generative AI solutions within an enterprise context. Focus on balancing innovation with governance, data privacy, and the specific challenges of integrating LLMs into existing workflows.

Model Monitoring and Fail-Safe Mechanisms

What to coverDetail your experience in establishing monitoring and governance frameworks. Explain how you design fail-safe mechanisms to detect model drift or performance degradation and the automated actions taken to mitigate these risks.

Cloud-Native Data Science on Azure

What to coverDiscuss your experience deploying ML solutions on cloud platforms like Azure. Highlight how you leverage cloud-native services for data processing, model training, and scaling, and how you manage costs and security.

Big Data Processing with Spark and Hadoop

What to coverExplain how you handle complex, large-scale datasets using distributed computing frameworks. Provide examples of optimizing data pipelines for performance and reliability in a production environment.

Advanced Statistical Modelling Applications

What to coverDescribe a scenario where you applied advanced statistical techniques to solve a complex business problem. Focus on the methodology, the validation process, and how you ensured the results were actionable for stakeholders.

Rapid Prototyping and POC Delivery

What to coverExplain your process for delivering rapid Proof of Concepts (POCs) while ensuring they align with long-term architectural goals. Discuss how you balance speed with the need for technical rigor.

Behavioural practice

Leading Technical Design Discussions

What to coverDescribe a time you led a complex technical design discussion with cross-functional teams. Focus on how you managed differing opinions, ensured alignment with business goals, and reached a consensus.

Translating Business Problems into Technical Solutions

What to coverProvide an example of how you partnered with non-technical stakeholders to define a business problem. Explain how you translated their needs into a technical roadmap and ensured the final solution delivered measurable impact.

Guiding Teams in High-Impact Environments

What to coverShare an experience where you mentored or guided a team through a high-pressure technical challenge. Highlight your leadership style, how you fostered innovation, and the outcome for the team and the project.

Navigating Agile Methodologies in Data Science

What to coverDiscuss a situation where you applied agile methodologies to a data science project. Explain how you adapted standard agile practices to accommodate the iterative and experimental nature of ML development.

Sharpen your knowledge

Topics to prepare

MLOps and Model Governance
The role requires establishing monitoring, governance, and fail-safe mechanisms for production models. Exercise: Draft a framework for a model monitoring dashboard that tracks data drift, latency, and prediction accuracy, and define a protocol for automated model retraining or rollback when performance thresholds are breached.
Scalable AI/ML Architecture
The position emphasizes building scalable architectures for enterprise-scale solutions. Exercise: Design a high-level architecture diagram for a Generative AI application that processes large-scale datasets, ensuring you account for data ingestion, model serving, and integration with cloud-native services.

Skills to revise

Cloud-Native Model Deployment (Azure)
The role prefers Azure for production-ready model deployment. Exercise: Practice deploying a containerized machine learning model using Azure Machine Learning services, focusing on setting up CI/CD pipelines to automate the deployment process.
Big Data Processing with Spark
Experience with big-data technologies like Spark is required for handling complex datasets. Exercise: Optimize a PySpark script for a large-scale data transformation task, focusing on minimizing data shuffling and effectively utilizing cluster resources.
Stakeholder Management and Technical Leadership
You will lead design discussions and partner with cross-functional teams. Exercise: Prepare a case study from your past experience where you translated a complex, ambiguous business problem into a technical AI/ML roadmap, emphasizing how you communicated trade-offs to non-technical stakeholders.

Your preparation checklist

Prepare a technical case study on MLOps and model governance
Draft a structured walkthrough of a past project where you designed and deployed a production-ready ML system. Focus on how you implemented monitoring, fail-safe mechanisms, and governance to ensure long-term model reliability in an enterprise environment.
Map your experience to large-scale AI/ML architectures
Create a summary of your experience designing scalable architectures using cloud platforms like Azure, AWS, or GCP. Be prepared to discuss how you integrated big-data technologies such as Spark or Hadoop to handle complex datasets.
Develop a strategy for stakeholder management in technical projects
Prepare specific examples of how you have translated complex business problems into technical AI/ML solutions. Highlight your approach to leading cross-functional teams and managing expectations during the lifecycle of an agile project.
Formulate questions regarding the Office of the CTO's AI roadmap
Prepare thoughtful questions about the current AI/ML challenges within the semiconductor industry, the specific tools or frameworks currently favored in their Azure environment, and how the team balances rapid POC delivery with long-term production stability.

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