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Full-Time

Data Engineer

R&D Informatics

Posted on 4/25/2024

Terray Therapeutics

Terray Therapeutics

51-200 employees

Discovers and develops small molecule drugs

Data & Analytics
Hardware
AI & Machine Learning
Biotechnology
Healthcare

Compensation Overview

$132k - $198kAnnually

+ Option Plan + 401K Contribution

Mid

Arcadia, CA, USA

Category
Data Engineering
Data & Analytics
Required Skills
Python
SQL
Java
AWS
Snowflake
Requirements
  • BS/MS/PhD in Computer Science or related Life Science discipline and 4+ years industry experience
  • Highly proficient in SQL and interfacing with databases in code
  • Significant experience in relational and non-relational database design
  • Experience building out data platforms
  • Proficiency in Python, Java, or Scala
  • Familiarity with Snowflake, AWS cloud resources and API design
  • Familiarity with containerization tools such as Docker and Kubernetes
  • Knowledge of coding best practices, including standards, reviews, version control, and unit testing
  • Excellent communication skills and demonstrated experience working with cross-functional teams
  • Knowledge of general biotech wet-lab operations (direct or indirect) and LIMS platforms such as Benchling, Signals Notebook, and Hamilton Instinct
  • Chromatography Data System experience and database administration is a plus
Responsibilities
  • Design, configure, optimize, and maintain database schemas and collaborate to implement data migration solutions
  • Design and implement new dashboards and user interfaces to help our wet-lab and computational scientists’ access and analyze their data
  • Design, develop, test, and maintain informatics infrastructure (data pipelines, storage, processing) that supports downstream data-driven applications and stakeholders
  • Work with both wet-lab and computational scientists to design and develop connections to our informatics systems API and database
  • Manage, improve, and customize our research informatics platforms

Terray Therapeutics focuses on discovering and developing small molecule drugs using a combination of experimental techniques and computational methods. The company generates a large dataset of over two billion unique target-ligand binding measurements, adding 150 million new measurements each month. This extensive data allows for precise predictions of molecular properties through deep learning models. Terray employs a fast design-make-test-analyze cycle, completing each cycle in under a month, which enables efficient exploration of chemical compounds and rapid identification of effective small molecules for various therapeutic targets. While the primary focus is on immunology, Terray collaborates with pharmaceutical and biotech companies to tackle a wide range of therapeutic challenges, creating additional revenue opportunities alongside its own drug discovery initiatives.

Company Stage

Series A

Total Funding

$80M

Headquarters

Pasadena, California

Founded

2018

Growth & Insights
Headcount

6 month growth

10%

1 year growth

32%

2 year growth

83%
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Simplify's Take

What believers are saying

  • Collaborations with industry giants like Bristol Myers Squibb and investments from NVIDIA highlight Terray's strong market position and growth potential.
  • The appointment of experienced leaders like Sudha Parasuraman and John Maraganore strengthens the company's strategic direction and operational expertise.
  • Terray's platform, which integrates chemical experimentation and computation, accelerates the drug discovery process, potentially leading to faster development of effective therapies.

What critics are saying

  • The reliance on generative AI and computational approaches may face skepticism and slower adoption within the traditionally conservative pharmaceutical industry.
  • High operational costs associated with maintaining a vast experimental dataset and rapid design cycles could strain financial resources.

What makes Terray Therapeutics unique

  • Terray Therapeutics uniquely combines wet lab science with generative AI, creating a vast experimental dataset that enhances the accuracy of molecular property predictions.
  • Their rapid design-make-test-analyze cycle, which takes less than a month per target, allows for efficient exploration of chemical space, setting them apart from traditional drug discovery methods.
  • Partnerships with major pharmaceutical companies like Bristol Myers Squibb provide additional revenue streams and validate their innovative approach.
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