Data Scientist 4

The group you’ll be a part of

The Office of the CTO is where innovation takes center stage. We inspire our global technical community to take on grand challenges, understand emerging trends, identify the critical inflections, and drive our sustainability, Environment, Social, and Governance (ESG) practices that will define the next generation of semiconductors and continued impact.

The impact you’ll make

In this role, you will directly contribute to building scalable AI and machine learning solutions that empower our teams to make faster, smarter decisions. You’ll help shape the next generation of AI capabilities by designing modern architectures, deploying production‑grade models, and driving innovation across the organization.

What you’ll do

• Design, develop, and deploy end‑to‑end AI/ML and Generative AI solutions that support critical business needs.
• Build scalable machine learning architectures and evaluate modern tools, frameworks, and platforms to strengthen our data science ecosystem.
• Develop production‑ready models and deliver solutions on cloud environments (preferably Azure) using strong MLOps practices.
• Partner closely with data scientists, engineers, and business stakeholders to translate business problems into technical solutions with measurable impact.
• Lead technical design discussions, guide teams through complex AI/ML challenges, and foster a collaborative innovation culture.
• Establish monitoring, governance, and fail‑safe mechanisms to ensure model reliability and continuous improvement.
• Deliver rapid POCs and extract meaningful insights from diverse and complex datasets.
• Work Schedule: Standard business hours with flexibility to collaborate across teams.

Who we’re looking for

• 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 (e.g., Hadoop, Spark).
• Proven understanding of MLOps, model deployment, and ML fail‑safe principles.
• Hands‑on experience in leading projects using agile methodologies.
• Strong communication and stake holder management skills.

Minimum 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.

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

Our commitment

We believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.

Lam Research ("Lam" or the "Company") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.

Lam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories – On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.