Sr. Data Scientist

Drive decision intelligence for portfolio prioritization, investment/resource allocation, and capacity planning through optimization, simulation, and predictive modeling.

Responsibilities

• Develop and solve optimization models for portfolio prioritization, investment and resource allocation, and capacity planning to maximize strategic value under constraints.
• Build simulation models such as Monte Carlo and discrete-event simulation to stress-test portfolio plans, measure risk and uncertainty, and compare outcomes across scenarios.
• Build, validate, and iterate predictive models to estimate delivery timelines, portfolio outcomes, and investment performance.
• Design MCP (Model Context Protocol) interfaces to expose optimization, simulation, and predictive capabilities to Planview Anvi and other AI agents.

Requirements

• Master’s degree in Industrial Engineering, Operations Research, Statistics, Data Science, or a related field.
• 5+ years of experience with Python and SQL, plus familiarity with pandas and scikit-learn.
• Experience applying optimization and/or simulation methods, including linear/integer programming, heuristics, Monte Carlo, and/or discrete-event simulation.
• Proven ability to translate customer problems into data-driven product enhancements.
• Strong knowledge of machine learning algorithms and statistical methodologies.
• Strong written and verbal communication skills.

Technologies

• Python, SQL, pandas, scikit-learn
• Monte Carlo, discrete-event simulation
• Linear/integer programming
• MCP (Model Context Protocol), agent frameworks
• AWS Athena, AWS SageMaker, AWS Lambda, AWS Bedrock
• Large Language Models, knowledge graphs, ontologies

Preferred Qualifications

• Familiarity with portfolio management and planning frameworks such as SAFe, Lean Portfolio Management, Agile, or Waterfall.
• Hands-on AWS experience with Athena, SageMaker, Lambda, and Bedrock.
• Experience with optimization solvers or simulation libraries.
• Experience building with Large Language Models and agent frameworks, including exposure to MCP (Model Context Protocol).
• Basic understanding of knowledge graphs and ontologies.

Benefits

• 4 company-wide paid days off per year (Unplugged Days).
• Generous PTO offerings (region dependent).
• Region specific competitive benefit plans including parental leave, retirement, medical plans, wellness support, and remote work support.
• Paid time to volunteer through Planview’s Force for Good Week.
• Employee Referral bonus program.
• Weekly office lunches for hybrid team members and social events.
• Flexible work hours and environment.

Location: Austin, TX (onsite)