Data Collection Jobs in Austin, TX (NOW HIRING)

Planview has one mission: to build the future of connected work, from ideas to impact.

As the global leader in work and resource management, Planview helps organizations accelerate the achievement of what matters most, supporting our customers from need to speed, from passion to progress, and from overhead to optimization. We provide the industry’s most comprehensive solutions designed for strategic planning, portfolio and resource management, Lean and Agile delivery, product portfolio management, capability, and technology management (enterprise architecture), innovation management, and collaborative work and project management. Our connected platform of solutions underpins the business and digital transformations of more than 4,500 customers globally, including 59 of the Fortune 100.

At Planview, our people connections drive our innovation and success. Our global team of 1400+ work remotely and across our offices including Austin, Vancouver, Hod Hasharon, Bangalore, Sydney, and Stockholm. We’re proud of our world-class, connected culture built on our shared values, that supports our teams to be successful from anywhere.

Learn more about our portfolio at planview.com, and connect with us on LinkedIn, Instagram, and X.

#LI-CG1 #LI-Hybrid

Join our team as a Data Scientist in Austin, TX. This position will help integrate data science, optimization, and simulation capabilities within Planview's planning solutions. You will contribute
to data collection and analysis, build predictive models, and work with
Generative AI technologies including Planview Anvi.

• 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.

Required Qualifications:

• 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.

Preferred Qualifications:

• 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.

At Planview, you’ll join our global team and culture which reflects the vibrant communities in which our employees live and work. To support you at Planview, you will enjoy:

• Unplugged Days: 4 company-wide paid days off per year to recharge and relax.
• Generous PTO offerings (region dependent).
• Region specific competitive benefit plans to support you at any life stage 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/environment.

Now is a great time to join our team. If you are looking for a place to grow your career, innovate with best-in-class solutions, and build the future of connected work, Planview is the right place for you!

Planview is proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, marital status, disability, gender identity or Veteran status.  As part of our efforts to build a diverse workforce, we encourage applications from Indigenous, Black, and other racialized communities, 2SLGBTQIA+ communities, people with neurodivergence, immigrants, people with disabilities, and other equity deserving groups. In addition to building a diverse workforce, we are committed to fostering an inclusive and accessible experience.

Reasonable accommodations for the interview process can be requested by emailing recruitment@planview.com.

Required Qualifications:

• 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.

Preferred Qualifications:

• 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.