Data Scientist

Data Scientist

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips – the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world – like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world. 

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. 

Key Responsibilities

• Analyze business problems and stakeholder requirements to frame data science use cases, define objectives, success metrics, assumptions, and constraints.
• Acquire, profile, and integrate structured and unstructured data from enterprise sources; assess data quality, completeness, consistency, and suitability for the problem at hand.
• Perform exploratory data analysis (EDA), feature engineering, preprocessing, and visualization to uncover patterns, validate hypotheses, and prepare data for modeling.
• Determine the appropriate analytical or machine learning approach based on the business problem and data characteristics, including regression, classification, clustering, time-series, or rule-based methods when ML is not warranted.
• Develop, test, and compare candidate models using standard data science practices such as train/validation/test splits, cross-validation, performance measurement, error analysis, and hyperparameter tuning.
• Document methodology, assumptions, model performance, and limitations; communicate results, recommendations, and business implications clearly to technical and non-technical stakeholders.
• Deploy and operationalize models and data pipelines in collaboration with data/platform teams; ensure outputs are production-ready, scalable, monitored, and maintainable.
• Build and support downstream reports, dashboards, and visualizations that translate model outputs and analytics into actionable business insights.
• Monitor model health, data drift, and business impact after deployment; fine-tune models, features, or pipeline logic as needed to sustain performance.
• Follow established standards for coding, documentation, governance, reproducibility, version control, and responsible use of enterprise data and AI/ML methods.

Additional Job skills

• Preferred technical skills: Python, SQL, PySpark or Spark, machine learning libraries/frameworks, statistical analysis, data wrangling, and visualization tools such as Tableau or Power BI.
• Experience with Databricks or comparable cloud analytics platforms, model/pipeline operationalization, and production data workflows is preferred.
• Knowledge of deep learning and NLP/LLM concepts, techniques, and practical applications is preferred to support advanced text analytics, generative AI, and emerging AI/ML use cases.
• Familiarity with experiment design, model evaluation metrics, feature selection, and monitoring for drift or performance degradation is preferred.
• Working knowledge of data warehousing concepts, dashboard design, and communicating insights through visualization and storytelling is preferred.
• Experience with version control, reusable code development, and documentation of data science workflows is preferred.
• Bachelor’s or Master’s degree in Computer Science, Information Science, Statistics, Mathematics, or a related quantitative field.
• Typical experience: approximately 2–5 years of relevant experience in data science, advanced analytics, or machine learning solutions delivery.

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.

Experience Level

Mid Level