Data Science Engineer - Professional Services

Remote - Boston, Massachusetts, United States

TetraScience

The Tetra Scientific Data and AI Cloud is the only vendor-neutral, open, cloud-native platform purpose-built for science. Get next-generation lab data automation, scientific data management, and foundational building blocks of Scientific AI....

View company page

Who We Are

TetraScience provides the world’s first and only R&D Data Cloud, with a mission to transform life sciences R&D, accelerate discovery, and improve human life. Scientists at global pharma and biotech organizations rely on our innovative Tetra Data Platform for easy access to centralized, harmonized, and actionable scientific data to accelerate their digital lab transformation. With best-in-class SaaS performance, a team of industry innovators, and excellent product/market fit, Tetra is positioned to become an iconic life sciences software company.

What You Will Do

The TetraScience Professional Services Data Science Engineer will report into the Professional Services organization and will be responsible for the implementation of the TetraScience Data Platform for new clients. This entails execution on the project scope to ensure TetraScience meets or exceeds client expectations and fulfills their requirements.

Additional duties include research and prototype of data acquisition strategies for scientific lab instrumentation along with file parsers for instrument output files (.xlsx, .pdf, .txt, .raw, .fid, many other vendor binaries). Based on project scope, design and build data models, Python data pipelines, unit tests, integration tests and utility functions. As needed, build visualization, report, and dashboards using Spotfire, Tableau and Jupyter notebook for new clients.


Requirements

What You Have Done

  • 5+ years’ experience working with in Python and SQL.
  • AWS/GCP/Azure certification preferred.
  • Elasticsearch, science background, or experience with scientific instruments.
  • Experience with tools like Spotfire, Tableau, Jupyter notebook (any of them).
  • Excellent verbal and written communications skills; ability to explain technical information in non-technical language to drive progress.
  • Ability to manage multiple simultaneous projects, proactive troubleshooting skills and attention to detail.
  • Experience working in a customer facing role, implementing software or cloud-based software solutions, ideally in to the Life Sciences marketplace.
  • Bachelor’s degree in Computer Science, Statistics, Chemistry, Business or related field’ or equivalent work experience


Ideal if you have

  • Passion about science and building solutions to make the data more accessible to the end-users.
  • Intellectually curious: Unwavering drive to learn and know more every day.
  • Ability to think creatively on how to solve project risks without reducing quality.
  • Ability to distill and present complex information to a wide range of stakeholders.
  • Team player and ability to "roll up your sleeves" and do what it takes to make the team successful.
  • Experience in Life Sciences and Pharma workflows, especially with drug design and development, biologics, automation or new modalities.
  • Knowledge or understanding of GxP compliance-related activities such as Good Laboratory Practices (GLP) is ideal.

Benefits

  • 100% employer-paid benefits for all eligible employees and immediate family members.
  • Unlimited paid time off (PTO).
  • 401K.
  • Flexible working arrangements - Remote work + office as needed.
  • Company paid Life Insurance, LTD/STD.

Tags: AWS Azure Chemistry Computer Science Data pipelines Elasticsearch GCP Jupyter Pipelines Python R R&D Research Spotfire SQL Statistics Tableau

Perks/benefits: Flex hours Flex vacation Team events Unlimited paid time off

Regions: Remote/Anywhere North America
Country: United States
Job stats:  4  0  0
Category: Engineering Jobs

More jobs like this

Explore more AI, ML, Data Science career opportunities

Find even more open roles in Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), Data Engineering, Data Analytics, Big Data, and Data Science in general - ordered by popularity of job title or skills, toolset and products used - below.