Scientific Implementation Analyst | STEM / R&D Software

Greenway Collins; New York (NY); Hybrid; USD 75,000 - 85,000; Full time

Job summary

Greenway Collins in New York is hiring an entry-level Scientific Implementation Analyst to sit at the intersection of science, data, software and customer-facing work. You’ll implement the platform, structure complex datasets and support onboarding for enterprise R&D customers.

You’ll work with scientists and technical teams to translate workflows into usable software, with hands-on data work and collaboration across projects.

Qualifications

• Hands-on lab, research, internship or experimental data experience.
• Strong academic background, ideally from a highly selective university.

Responsibilities

• Work directly with R&D customers to understand scientific workflows and experimental data.
• Help configure and implement software for technical users.
• Analyse, clean and restructure scientific datasets.
• Support customer meetings, onboarding and implementation projects.
• Work with senior implementation leads and progress towards owning customer projects independently.
• Build deep exposure to enterprise scientific software, data workflows and applied R&D operations.

Skills

Excel

Python exposure

Data analysis

Stakeholder communication

Education

STEM degree (chemistry / materials science / chemical engineering / biology / bioengineering / physics)

Job description

A high-growth scientific software company, founded by MIT and Stanford engineers and backed by leading investors, is hiring a Scientific Implementation Analyst in New York.

The company builds enterprise software used by R&D teams across chemicals, materials science, batteries, pharma, food and advanced manufacturing. Its platform acts as the system of record for scientists at major global companies, helping them structure experimental data, run better research workflows and make faster technical decisions.

This is an entry-level role designed for a high-calibre STEM graduate or early-career scientific operator who wants to sit at the intersection of science, data, software and customer-facing work.

You’ll work with scientists, researchers and technical teams to implement the platform, structure complex experimental datasets, support customer onboarding, and translate scientific workflows into usable software systems.

What you’ll do

• Work directly with R&D customers to understand scientific workflows and experimental data
• Help configure and implement software for technical users
• Analyse, clean and restructure scientific datasets
• Support customer meetings, onboarding and implementation projects
• Work with senior implementation leads and progress towards owning customer projects independently
• Build deep exposure to enterprise scientific software, data workflows and applied R&D operations

Best fit

• STEM degree in chemistry, materials science, chemical engineering, biology, bioengineering, physics or a related technical field
• Strong academic background, ideally from a highly selective university
• Hands-on lab, research, internship, academic research or experimental data experience
• Strong Excel / structured data skills
• Python, scripting or data analysis exposure is useful
• Comfortable speaking with scientists, technical stakeholders and enterprise customers
• Practical, polished and commercially aware, not purely theoretical

This is particularly well-suited to someone who has enjoyed scientific research but wants a faster-moving role with more ownership, customer exposure and commercial impact than a traditional lab or academic path.

Practical details

• Location: New York City metro area
• Working pattern: Hybrid, with regular in-office presence required
• Compensation: $75,000-85,000 base, plus equity, bonus and benefits
• Visa: candidates should already have US work authorisation and should not require sponsorship within the first year
• No relocation support available

This is not a pure research role and not a generic customer support role. It is a scientific software implementation role for someone technical enough to work with experimental data and polished enough to sit in front of enterprise R&D customers.