Sr Data Scientist

Who We Are

Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.

What We Offer

Location:

Bangalore,IND

You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.

At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .

Role Overview

As a Data Scientist, you will develop data-driven insights, predictive models, and optimization solutions that improve semiconductor manufacturing operations. You will work closely with customers, domain experts, and engineering teams to transform large-scale manufacturing and simulation data into actionable recommendations and intelligent decision-support capabilities.

The role combines statistical analysis, machine learning, optimization, simulation, and experimentation. You will contribute to both customer projects and internal innovation efforts while helping bridge the gap between business challenges and technical solutions.

Key Responsibilities

Data Analysis & Insights

Collect, process, and analyze manufacturing, simulation, and operational datasets.

Perform exploratory data analysis (EDA) to identify trends, bottlenecks, anomalies, and opportunities for improvement.

Develop statistical models and analytical frameworks to support operational decision making.

Translate complex analytical findings into clear business recommendations.

Modeling & Optimization

Design, develop, and validate predictive models for forecasting, classification, anomaly detection, and optimization use cases.

Apply operations research, statistical learning, and optimization techniques to solve manufacturing challenges.

Contribute to hybrid solutions that combine analytics, machine learning, and simulation technologies.

Design experiments and perform rigorous analysis to validate model effectiveness and business impact.

Simulation & Manufacturing Intelligence

Work with simulation technologies, including discrete-event simulation (DES), to evaluate manufacturing scenarios and operational strategies.

Analyze simulation outputs and combine them with historical operational data.

Develop methodologies that improve planning, scheduling, resource utilization, and factory performance.

Collaboration & Communication

Work directly with customers, domain experts, and project teams to understand business requirements.

Present technical findings and recommendations to both technical and non-technical audiences.

Document datasets, methodologies, experiments, and analytical conclusions.

Contribute to customer workshops, solution design sessions, and project reviews.

Research & Innovation

Stay current with developments in data science, statistics, optimization, and applied machine learning.

Evaluate emerging methodologies and identify opportunities to improve customer outcomes.

Support research initiatives focused on manufacturing intelligence and decision-support technologies.

Required Skills

Strong foundation in statistics, mathematics, and data analysis.

Experience developing predictive and analytical models.

Strong Python programming skills.

Hands-on experience with Pandas, Polars, NumPy, Scikit-learn, and related analytical tooling.

Experience with feature engineering, exploratory analysis, and model evaluation.

Understanding of optimization techniques, statistical experimentation, and hypothesis testing.

Strong problem-solving and analytical thinking skills.

Ability to communicate technical concepts to diverse stakeholders.

Preferred Skills

Experience with discrete-event simulation or operations research.

Experience with manufacturing, scheduling, supply chain, or industrial systems.

Familiarity with machine learning deployment and MLOps concepts.

Experience working with high-dimensional or time-series datasets.

Knowledge of semiconductor manufacturing or advanced industrial environments.

Experience - 8+ Years

About the Team

You will be joining a highly experienced team of data scientists, AI engineers, software developers, and cloud architects, many with over a decade of experience delivering innovative solutions across multiple industries. Our work spans embedded software, renewable energy optimization, advanced scheduling systems, and AI-driven decision support platforms for multi-billion-dollar semiconductor fabrication facilities.

At Applied Materials, your work will directly influence customer outcomes and business success. As a focused and highly collaborative team, every member plays a meaningful role in shaping both technical solutions and company direction.

No two days are the same. You'll face new challenges, learn continuously, and contribute to solving some of the most difficult problems in industrial AI and optimization. Our customers engage us because they need solutions that go beyond conventional approaches, giving our team the opportunity to create truly unique and impactful technology.

Additional Information

Time Type:

Full time

Employee Type:

Assignee / Regular

Travel:

Yes, 10% of the Time

Relocation Eligible:

Yes

Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.