Overview
We are building a commercial insights platform that our field, brand, medical and market access teams will use to make decisions at launch and for years afterward.
You would be the first engineering hire on it. You will make the architectural decisions, stand up the infrastructure, build the backend and the services, and own how the whole thing is deployed, secured and released.
You will then own it in production. This role builds the platform and then maintains, runs and extends it as the business grows into it. It is a permanent position on a system with a long life ahead of it, not a fixed-term project that ends at a launch date. The commercial organization has other data and software needs beyond this platform, so the work is likely to broaden over time rather than narrow.
You will work directly with the Director of Commercial Data Science, who owns the AI layer and the governed business definitions, alongside a Senior Data Scientist who builds the analytics and a Design Engineer who owns the interface layer. You own everything required to turn that work into software people can open and rely on.
The first production release is targeted within six months of the team forming, which means the foundational decisions are yours to make and yours to live with. Very few roles offer this much architectural ownership with this much of the hard analytical thinking already done, on something the leadership of the company is actively watching rather than something buried three layers down.
Responsibilities
- Make the foundational architecture decisions and be accountable for them: hosting shape, application framework, how the application reads from our Databricks warehouse, authentication and authorization, environments, release process.
- Deliver the first vertical slice early. One real measure taken end to end into production: warehouse to API to authenticated web page to rendered chart, deployed, logged, access-controlled. Every part of the platform after it reuses that path.
- Build the backend services that expose governed data and generated summaries to the interface layer.
- Own the cloud environment. Provision it, secure it, monitor it, and be able to explain and defend it to our IT and information security teams.
- Own engineering practice for a team that does not yet have any: repository structure, code review, testing, CI/CD, documentation, dependency and secrets management.
- Run what you build. Monitoring, alerting, incident response, performance, cost, dependency upgrades and the steady stream of changes that follow real users.
- Build and ship product surfaces alongside the Design Engineer, who leads interface design and front-end craft. You will both write front-end code.
- Productionize the AI layer. The prompting, retrieval and evaluation logic is designed by the Director in Python; you make it run reliably, observably and within cost.
- Work AI-assisted, deliberately. This is a small team delivering what would ordinarily take more. Using modern AI development tooling well is part of the job, not a personal preference.
Qualifications
- BS in computer science, software engineering, or a related quantitative or engineering discipline with 8+ years building and shipping production web applications, including at least two where you were the lead or sole engineer accountable for architecture.
- MS or PhD with 5+ years building and shipping production web applications, including at least two where you were the lead or sole engineer accountable for architecture.
- Eight or more years building and shipping production web applications, including at least two where you were the lead or sole engineer accountable for architecture.
- You have taken at least one substantial system from nothing to live, and can walk through the decisions you made, which ones were wrong, and what you would do differently.
- You have owned a system in production over time, not only delivered one. You can talk about what broke, what you learned from operating it, and how the design changed as a result.
- Strong full-stack capability. A modern typed backend and a modern component-based front-end. We are not prescriptive about the exact stack, but you must have real depth on both sides rather than familiarity with one and exposure to the other.
- Cloud infrastructure ownership, ideally Azure: compute, networking, managed databases, infrastructure as code, CI/CD pipelines.
- Security competence sufficient to pass an information security review. Identity and access management on least privilege, network isolation, encryption in transit and at rest, secrets management, audit logging. You do not need to be a security specialist. You do need to build systems that survive scrutiny.
- Experience with regulated or high-consequence data: healthcare, financial services, insurance, or a comparable domain where an incorrect number has real consequences.
- Data literacy. You can read and write SQL against a cloud data warehouse, reason about why a query is slow and what it costs, understand what a cohort definition is, and present analytical output without misrepresenting it. You will not be asked to build statistical models.
- The AI layer is authored in Python by the Director, and you will be the one making it run reliably in production. This is the one place we are prescriptive about language; the application stack itself is open.
- Demonstrated fluency with AI-assisted development, for example Claude Code, GitHub Copilot or equivalent. In interview we will ask what you shipped with them and where they failed you.
- You can work directly with non-engineers. There is no product manager. Requirements will come from brand leads, field leadership and medical, and you will help turn conversations into specifications.
- On site in Hampton, New Jersey four days per week.
Nice to have
- Pharmaceutical or life sciences commercial experience, especially with medical or pharmacy claims, CRM platforms, or launch analytics. This is our strongest differentiator among otherwise comparable candidates.
- Databricks, Snowflake or comparable lakehouse experience.
- Experience building conversational or LLM-backed product features: streaming, retrieval, citation display, evaluation.
- Data visualization or dashboard engineering.
- Experience as an early engineer at a startup, or otherwise operating without existing infrastructure.
- Experience working alongside external vendors and consultancies.
Compensation StatementThe expected base salary for this position is: $153616.00 to $199501.00. We are committed to compensating employees equitably based on several factors including experience, education, licensure/certifications, skill level, location of the position, and availability of similar talent in a competitive market. Subject to eligibility and applicable plan terms, compensation includes base salary, annual discretionary bonus, long term incentive, 401(K) plan with employer contribution, health care and other insurance benefits (for employee and family), paid holidays, vacation, and sick days. A summary of our benefits can be found on our careers page
