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# Data Scientist

- **Company:** Planview Incorporated
- **Location:** Austin, TX, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Agile Methodology, Artificial Intelligence, Discrete Event Simulation, Graph Database, Integer Programming, Python (Programming Language), Machine Learning, SQL Databases, Large Language Models, Multi-Agent Systems, Pandas, Scikit Learn, Planview, Virtual Agents
- **Published:** July 25, 2026
- **Apply:** https://www.austinjobsite.com/job.asp?id=3331751306&tx=JT10193UTI&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

* Master's degree in Industrial Engineering, Operations Research, Statistics,
Data Science, or a related field.

 * 3-7 years experience in Python and SQL, along with familiarity with libraries such as
pandas and scikit-learn.

 * Experience with optimization and/or simulation methods (e.g.,
linear/integer programming, heuristics, Monte Carlo, discrete-event
simulation).

 * Demonstrated experience in translating customer challenges into data-
driven product enhancements.

 * Strong knowledge of machine learning algorithms and statistical
methodologies.

 * Strong written and verbal communication skills., * Familiarity with portfolio management and planning frameworks (e.g.,
SAFe, Lean Portfolio Management, Agile, Waterfall).

 * Hands-on experience with AWS services such as Athena, SageMaker,
Lambda, and Bedrock.

 * Experience with optimization solvers or simulation libraries.
 * Experience building with Large Language Models and agent frameworks,
including exposure to the Model Context Protocol (MCP).

 * Basic understanding of knowledge graphs and ontologies.

## Description

* Optimization: Formulate and solve optimization problems for portfolio
prioritization, investment and resource allocation, and capacity planning
to maximize strategic value under constraints.

 * Simulation: Build simulation models (e.g., Monte Carlo, discrete-event) to
stress-test portfolio plans, quantify risk and uncertainty, and forecast
outcomes across scenarios.

 * Predictive Modeling: Build, test, and refine models to predict delivery
timelines, portfolio outcomes, and investment performance.

 * Agentic AI: Design MCP (Model Context Protocol) interfaces that expose
optimization, simulation, and predictive capabilities to Planview Anvi and
other AI agents.


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