Data Scientist|Foundry/Palantir

KLA•Milpitas, CA

20h•$110,000 - $187,000

About The Position

A Data Scientist with operations experience leverages advanced analytical methods, statistical modeling, and data engineering skills to improve operational performance and decision‑making across the business. This role partners closely with operations, supply chain, purchasing and planning to identify efficiency opportunities, optimize processes, and develop data‑driven solutions that enhance productivity and scalability. Key responsibilities typically include designing and maintaining predictive models, building dashboards and performance metrics, analyzing process bottlenecks, and developing automated reporting, and analytic applications or optimization tools. The role also requires translating complex data insights into clear recommendations for operational leaders, and ensuring data quality, governance, and reliability across operational data pipelines. An operations‑focused Data Scientist blends technical expertise with strong business and process understanding, enabling them to drive measurable improvements in throughput, cost, quality, and overall operational excellence.

Requirements

Advanced degree (Master’s) in Data Science, Operations Research, Industrial Engineering, Applied Statistics, Computer Science, or a related quantitative field.
Hands‑on experience with operations‑focused analytics, such as supply chain optimization, forecasting, workforce planning, manufacturing analytics, or process improvement.
Familiarity with Palantir technologies, including Foundry and Ontology development, with the ability to build, maintain, and operationalize data objects and pipelines in the platform.
Proficiency in statistical modeling and machine learning, including time‑series forecasting, optimization algorithms, clustering, regression, and anomaly detection.
Strong data engineering capabilities, including experience with ETL pipelines, data modeling, and building scalable data architectures.
Experience working with cloud platforms (Azure, AWS, GCP) and modern data stack technologies (Databricks, Snowflake, Synapse, BigQuery, etc.).
Advanced programming skills in Python, SQL, and familiarity with frameworks like PySpark or Spark SQL for large‑scale data processing.
Experience with visualization tools such as Power BI, Tableau, or Looker for building operational dashboards and KPI tracking.
Strong understanding of Lean, Six Sigma, or other continuous improvement methodologies.
Ability to translate complex analytics into clear operational insights for non‑technical stakeholders.
Experience working in cross‑functional environments with operations, product, finance, and engineering teams.
Proven track record of driving measurable improvements in efficiency, throughput, cost reduction, or operational quality.
Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years

Responsibilities

Designing and maintaining predictive models
Building dashboards and performance metrics
Analyzing process bottlenecks
Developing automated reporting, and analytic applications or optimization tools
Translating complex data insights into clear recommendations for operational leaders
Ensuring data quality, governance, and reliability across operational data pipelines