Senior Applied Mathematician

Why is this job for you?

We are focused on discovering new patterns in big data. These pattern discoveries will significantly and positively impact people lives. Our initial work is in the biomedical space, focusing on identifying the patterns related to breast cancer, lung cancer, and other diseases such as Chron’s disease. This job is for you because we are taking on what has been viewed as an intractable problem. We learn daily from each other in the space of mathematics, machine learning, data science, bioinformatics and biology, with each of our experts working at or beyond the state of the art. Learning, sharing ideas, and synthesizing information is our passion. Making a positive impact by combining leading-edge research with emerging technologies is our mission. That’s why we are here. We’ll take on additional domains such as finance, transportation, energy, etc. as time and resources allow.

You are well suited for this job because you have experience in mathematics spanning many areas. You have done work in the areas of decision trees; understanding partitioning and/or clustering of data in large datasets. You are familiar with topological data analysis techniques and have good intuition when working with high dimensional datasets. You know dimensional reduction strategies and the associated tradeoffs/remedies of the various techniques. Most importantly, you like interesting challenges in applied mathematics and enjoy doing research on best techniques. You enjoy working with datasets to understand the nature of both the algorithm(s) and the dataset(s); how tuning of algorithms and/or hyperparameters can positively influence the results while retaining the data integrity. An ideal candidate would have the following traits in the realm of mathematics: passion, experience, and intuition.

Key Responsibilities

  • Leverage existing work which reduces high dimensional complex datasets to lower dimensional relationships.
  • Design and develop algorithm to solve data analysis challenges, introducing and applying novel mathematics techniques aligned with partitioning data spaces, clustering data, and topological data analysis.
  • Guidance on staging or coupling various applied mathematics techniques (such as Topological Data Analysis) balancing effectiveness with time-to market.
  • Learn new approaches, work collaboratively, share expertise freely - insights with data, algorithms and novel strategies with other team members.
  • Suggest POC experiments with the target data sets including expected outcomes, required resource estimates and timelines.
  • Build initial prototypes of specific algorithm chains in C/C++, Python and/or various packages such as Scikit-learn.

Required Qualifications

  • PhD in Applied Mathematics or a master’s with equivalent experience.
  • Deep knowledge of mathematics related to decision trees, partitioning data spaces, topological data analysis and clustering strategies.
  • Ability to think outside traditional algorithms; translating concepts from one domain to applications in other fields.
  • Comfortable working with high dimensional, complex data spaces. Has experience with multiple dimensional reduction techniques.
  • Able to share knowledge with the company team.
  • Effective communicator.
  • Able to write code in C/C++, or similar languages.

Preferred Qualifications

  • 15 years’ work in one of the following fields: advanced data science, applied mathematics, and topological data analysis.
  • Experience working with high dimensional sparse datasets.
  • Working knowledge of machine learning techniques, including comparative analysis of the various techniques as applied to different problem spaces.
  • Experience working with statistical analysis tools such as R, MATLAB, SPSS or SAS.

This position is located in Redmond, Washington

Travel is minimal, less than 25%

Relocation Seattle area is required within 45 days of hire date.

Pattern Computer is an equal-opportunity employer

Is this right for you? Send us an e-mail at