Scientific Data Engineer at TetraScience

Boston, Massachusetts, United States -

Full Time

Start Date

Immediate

Expiry Date

21 Dec, 25

Salary

0.0

Posted On

22 Sep, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, SQL, Data Modeling, Data Analytics, Data Visualization, Streamlit, Tableau, Jupyter Notebook, Problem-Solving, Communication, Attention to Detail, Project Management, Team Player, AI, LLM, Intellectual Curiosity

Industry

Software Development

Description

Who We Are TetraScience is the Scientific Data and AI Cloud company. We are catalyzing the Scientific AI revolution by designing and industrializing AI-native scientific data sets, which we bring to life in a growing suite of next gen lab data management solutions, scientific use cases, and AI-enabled outcomes. TetraScience is the category leader in this vital new market, generating more revenue than all other companies in the aggregate. In the last year alone, the world’s dominant players in compute, cloud, data, and AI infrastructure have converged on TetraScience as the de facto standard, entering into co-innovation and go-to-market partnerships: Latest News and Announcements | TetraScience Newsroom In connection with your candidacy, you will be asked to carefully review the Tetra Way letter, authored directly by Patrick Grady, our co-founder and CEO. This letter is designed to assist you in better understanding whether TetraScience is the right fit for you from a values and ethos perspective. It is impossible to overstate the importance of this document and you are encouraged to take it literally and reflect on whether you are aligned with our unique approach to company and team building. If you join us, you will be expected to embody its contents each day. What You Will Do Research data acquisition strategy for scientific lab instrumentation Research and productionize file parsers for instrument output files (.xlsx, .pdf, .txt, .raw, .fid, many other vendor binaries) Design and build data models and the corresponding data pipelines, unit tests, integration tests, and reusable utility functions Cross-analyze instrument data with the same instrument type or scientific workflow to design common data model components. Build visualization, report, and dashboards using Streamlit, Tableau, Jupyter notebook, etc. Drive value for the customers - test and make sure the solution fulfills their requirements and provides value What You Have Done Must have >3 years experience in Python and SQL Must have a degree in biology or chemistry or relevant fields OR > 3 years of work experience in pharmaceutical or biotech industry in scientific data engineering, data modeling, or data analytics. Experience with data plotting dashboarding tools like Streamlit, Tableau, Jupyter Notebook is a plus Passionate about data problems and using AI and LLM to come up with creative ideas Excellent communication skills, attention to detail, and the confidence to take control of project delivery Quickly understand a highly technical product and effectively communicate with product management and engineering Proactive problem-solving skills High-bandwidth: thrives when managing multiple simultaneous projects Intellectually curious: Unwavering drive to learn and know more every day Ability to think creatively about how to solve project risks without reducing quality Team player and ability to "roll up your sleeves" and do what it takes to make the team successful 100% employer-paid benefits for all eligible employees and immediate family members Unlimited paid time off (PTO) 401K Flexible working arrangements - Remote work Company paid Life Insurance, LTD/STD A culture of continuous improvement where you can grow your career and get coaching We are not currently providing visa sponsorship for this position

Responsibilities

The Scientific Data Engineer will research data acquisition strategies for scientific lab instrumentation and productionize file parsers for various instrument output files. They will also design and build data models and pipelines, ensuring solutions fulfill customer requirements and provide value.