Data Scientist / Visualization Specialist

Key Responsibilities

• Design and develop user-facing analytic applications and dashboards in Palantir Foundry.
• Translate complex data models and analytics into clear, compelling visualizations.
• Build interactive, data‑driven UIs that embed analytics and metrics into workflows.
• Collaborate with data scientists and engineers to integrate analytics, AI outputs, and ontologies.
• Apply UX/UI best practices to deliver accessible, intuitive, and scalable applications.
• Work within an Agile/Scrum environment to rapidly iterate on solutions.
• Communicate insights effectively to both technical and non‑technical stakeholders.

Requirements & Tech Stack

• Experience building data‑driven applications or dashboards.
• Strong data visualization and storytelling skills.
• Experience with BI/visualization tools (PowerBI, Tableau, Qlik, or similar).
• Proficiency in Python.
• Experience with Python visualization libraries (plotly, matplotlib, seaborn).
• Understanding of data analysis and exploratory data analysis (EDA).
• Experience wrangling and aggregating data from multiple data sources.
• Solid grasp of UX/UI best practices for analytic applications.
• Comfort working in a dynamic Agile/Scrum environment with evolving or loosely defined requirements.
• Excellent collaboration and communication skills.
• Experience with Palantir Foundry, Databricks, or similar data platforms.
• Ability to obtain or maintain a Secret Clearance.

Preferred Experience

• Knowledge of TypeScript for front‑end or application‑layer development.
• Experience building rapid analytic demos or apps (e.g., Streamlit).
• Hands‑on experience with Palantir Foundry components (pipelines, transforms, Workshop, Ontologies, AIP), especially Workshop and Ontologies.
• Experience with data platform operations, monitoring, or optimization.
• Experience building and maintaining ETL pipelines supporting analytic applications.
• Familiarity with AI/ML and statistical modeling tools/libraries (e.g., scikit‑learn, statsmodels, Prophet; NLP tools like spaCy or NLTK).
• Proficiency with Python for analytics and data pipelines (pandas, polars, PySpark).