Scientific Data Architect - New York, NY at TetraScience

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

10 Feb, 26

Salary

0.0

Posted On

12 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Scientific Data, AI, Data Models, Python, Data Visualization, Cloud Environments, Exploratory Data Analysis, Collaboration, Communication, Machine Learning, Data Integration, Prototyping, Workflow Optimization, Consulting, Problem Solving, Continuous Improvement

Industry

Software Development

Description

Who We Are TetraScience is the Scientific Data and AI 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. Who You Are You are a product-minded, outcome-obsessed driver of technical scientific solutions. You a high velocity self-starter. You refuse to let uncertainty obstruct your path to designing and building solutions. You roll up your sleeves, try things out, and get things done. You do not hesitate to prototype, demo, and build in order to accelerate delivery of products for your end users. You thrive in environments where you can collaborate with scientists, product managers, and engineers to transform complex scientific data into actionable outcomes. Your ability to engage with scientists and business leaders alike makes you a key player in maximizing the value of scientific data. With rich experience applying cutting edge data methodologies to the biopharma R&D domain, you bridge understanding between present-day pain points and generalizable solutions. You are an insatiable learner, with a track record of deeply learning new tools, methods, and domains. You fundamentally embody the principles of extreme ownership and have a demonstrated history of building extensible data models and applications for Biopharma end users to maximize value from their data via analysis and integration with AI/ML. This role will require extreme self-discipline and determination as we forge a category that will fundamentally and forever change the life science industry. What You Have Done PhD with 7+ years / Masters with 10+ years of industry experience in life sciences with extensive domain knowledge in drug discovery (target ID through lead optimization), preclinical development, CMC (all drug modalities), or product quality testing. Proven track record of defining, designing, prototyping, and implementing productized AI/ML-driven use cases in cloud environments Collaborated with cross-functional teams, including product managers, software engineers, and scientific stakeholders. Performed extensive exploratory data analysis and workflow optimization to enable scientific outcomes not previously possible. Engaged diverse audiences, from scientists to executive stakeholders using your excellent communication and storytelling abilities. Advised scientists in a consulting capacity to further research, development, and quality testing outcomes. What You Will Do You will be a critical team member in a unique partnership to industrialize Scientific AI. As such, you will engage directly with customers onsite a couple of days per week in the Copenhagen Region, building strong relationships, deeply understanding their scientific data challenges and requirements, and accelerating solutions. Design and implement extensible, reusable data models that efficiently capture and organize scientific data for scientific use cases, ensuring scalability and future adaptability. Translate scientific data workflows into robust solutions leveraging the Tetra Data Platform. Own, scope, prototype, and implement solutions including: Data model design (tabular & JSON) Python-based parser development. Lab software (e.g., ELN/LIMS) integration via APIs. Data visualization and app development in Python (using app frameworks like Streamlit and plotting tools like holoviews and Plotly) Collaborate with Scientific Business Analysts (SBAs), customer scientists and applied AI engineers to develop and deploy models (ML, AI, mechanistic, statistical, hybrid) Programmatically interrogating proprietary instrument output files. Dynamically iterate with scientific end users and technical stakeholders to rapidly drive solution development and adoption through regular demos and meetings Proactively communicate implementation progress and deliver demos to customer stakeholders. Collaborate with the product team to build and prioritize our roadmap by understanding customers’ pain points within and outside Tetra Data Platform. Rapidly learn new technologies (e.g., new AWS services or scientific analysis applications) to develop and troubleshoot use cases Competitive Salary and equity in a fast-growing company. Supportive, team-oriented culture of continuous improvement. Generous paid time off (PTO). Flexible working arrangements - Remote work when not at Customer Sites We are not currently providing visa sponsorship for this position.

Responsibilities

You will engage directly with customers to understand their scientific data challenges and accelerate solutions. Additionally, you will design and implement extensible data models and translate scientific data workflows into robust solutions.