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Data Scientist
Отличная вакансия для амбициозных специалистов: работа в быстрорастущем стартапе с product-market fit, сильная команда (YC alumni) и прямой доступ к сложным медицинским данным. Высокая автономность и щедрый пакет опционов компенсируют интенсивный график.
Сложность вакансии
Высокая сложность обусловлена требованием 'олимпийской' работоспособности (6-дневная рабочая неделя фаундеров) и необходимостью совмещать навыки Data Science, инженерии данных и управления продуктом. Работа с медицинскими данными (FHIR, HL7) добавляет специфическую отраслевую сложность.
Анализ зарплаты
Для позиции Data Scientist с опытом от 4 лет в Сан-Франциско рыночный диапазон составляет $160,000–$210,000 плюс значительный пакет опционов. Metriport предлагает конкурентоспособную компенсацию, которая, учитывая стадию роста компании, вероятно, находится в этих пределах.
Сопроводительное письмо
I am writing to express my strong interest in the Data Scientist position at Metriport. With over four years of experience in high-growth environments and a deep obsession with data integrity, I am drawn to your mission of revolutionizing healthcare data access. My background in building robust data models using dbt and SQL, combined with a product-owner mindset, aligns perfectly with your need for an analytics powerhouse who can bridge the gap between raw engineering and actionable insights.
In my previous roles, I have consistently focused on 'productizing' analytics, moving beyond simple reporting to create self-service tools and automated enrichment pipelines. I am particularly excited about the opportunity to apply AI/ML models to clinical data and work within your TypeScript/Python stack. Being a 'hacker at heart,' I thrive in high-autonomy environments where output is the primary metric, and I am eager to contribute to Metriport's scaling phase in San Francisco.
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Описание вакансии
Data Scientist
San Francisco, CA
About us
Metriport is an open-source data intelligence platform that helps healthcare organizations access and exchange patient data in real-time. We integrate with all major US healthcare IT systems and tap into comprehensive medical data for 300+ million individuals.
We've found product-market fit with multi-million ARR, 100+ customers (including Amazon, Color, and Strive Health), backing from top VCs, and years of runway. We're ready to scale. We're a tight-knit, high-performing team of mostly former founders (including two YC alumni). We're engineering-heavy, operate with minimal bureaucracy and high autonomy, and hire based on competence, not prestige. We push hard—founders work six days a week from our SF office—but give everyone freedom to craft their schedule. We measure output and we're committed to sustainable intensity.
About you
In a nutshell, we're looking for an analytics powerhouse who acts like a product owner. You aren't just a "dashboard builder"; you are the architect of our data ecosystem and the value we derive from it:
- You’re entrepreneurial-minded, with an olympian-level work ethic.
- You are obsessed with data integrity. If a metric is off by 1%, it keeps you up at night until you find the root cause.
- You believe that high-quality clinical data is the bedrock of excellent healthcare, and you’re excited to map complex patient records into structured, efficient warehouses for downstream use.
- You have a strong sense of ownership and the ability to lead cross-functional initiatives without hand-holding.
- You care more about the insights and the "so what?" than just the technical complexity of the query.
- When someone asks for a report by next week, you ask yourself "how can I build a self-service tool that answers this permanently by tomorrow?"
- You’re a hacker at heart, and you’re comfortable writing code to get the job done.
What you'll be doing
After quickly ramping up on our clinical data domain, your goal is to own the "brain" of Metriport. You will be the bridge between raw engineering output and actionable customer success. Specifically, day to day, this looks like:
- Owning data quality and analytic integrity within the product-engineering space: Ensuring that every chart, report, and insight we share—internally or externally—is 100% accurate and trustworthy.
- Owning the Analytics Stack: You will be the primary owner of our analytics tooling (e.g., Posthog) and ensure these systems are correctly implemented, optimized and accessible.
- Productizing Analytics: Designing and shipping analytic suites as core features of the Metriport platform, allowing customers to understand their patient population from our UI or their data warehouse.
- Advanced Projects:
+ Applying AI/ML models to our data warehouse to predict patient outcomes or identify gaps in care.
+ Writing Python or TypeScript to automate data enrichment or build custom internal tools.
+ Using LLMs or the like to help normalize messy clinical data into structured, searchable insights.
- Participating in a daily 30 minute remote stand-up at 7:30am PST Mon-Fri (our only regular mandatory meeting).
Requirements
- 4+ years of experience in a high-growth data science or data analytics role.
- Mastery of SQL: You can write complex, performant queries in your sleep.
- Tooling Expertise: Deep experience with product analytics tools (like Posthog, Mixpanel, or Amplitude) and BI platforms.
- Coding Proficiency: You are comfortable in Python (pandas/scikit-learn) or TypeScript for data manipulation and automation.
- Data Modeling: Experience with dbt or similar tools to transform raw warehouse data into clean, documented schemas.
- Location: You’re located in San Francisco or the Bay Area (or willing to relocate).
- Healthcare Plus: Experience with FHIR, HL7, or clinical data is a massive plus. Understanding how a patient moves through the healthcare system is the core of what we do.
Benefits
- Competitive equity + compensation package 🚀
- Full family Platinum health insurance, dental, and vision coverage 🦷
- 401(k) retirement plan + matching 💰
- Flexible work from home or in-office 🏢
- Healthy lunches are complimentary when working in-office (and breakfast + dinners as needed) 🍏
- Quarterly company off-sites with the team ⛷️
- MacBook provided by us 💻
- Unlimited PTO (we work hard, but trust you to take time you need to be at your best) 🧘♂️
Our tech
Our data lives in PostgreSQL, DynamoDB, S3, Snowflake, and a FHIR server. We use dbt for transformations and Posthog for product analytics. Our infrastructure is managed via AWS CDK, and our core platform is written in TypeScript and Python. We are looking for a generalist who can jump into any part of this stack to extract value.
Metriport provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity, or gender expression. We are committed to a diverse and inclusive workforce and welcome people from all backgrounds, experiences, perspectives, and abilities.
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Навыки
- TypeScript
- Python
- Pandas
- SQL
- dbt
- AWS CDK
- PostgreSQL
- Scikit-learn
- Snowflake
- HL7
- FHIR
- Data Modeling
- PostHog
Возможные вопросы на собеседовании
Проверка продуктового мышления и способности создавать масштабируемые решения вместо разовых отчетов.
Расскажите о случае, когда вместо подготовки разового отчета вы создали инструмент самообслуживания. Как это повлияло на работу команды?
Оценка технического мастерства в SQL и понимания производительности при работе с большими данными.
Опишите самую сложную SQL-задачу, которую вы решали. Как вы оптимизировали запрос для работы в Snowflake или PostgreSQL?
Проверка внимания к деталям и отношения к качеству данных, что критично для медицинского стартапа.
Что вы предпримете, если обнаружите расхождение в ключевой метрике на 1%? Опишите ваш процесс поиска первопричины.
Оценка опыта работы с современным стеком трансформации данных.
Как вы структурируете проекты в dbt для обеспечения чистоты и документированности схем в хранилище данных?
Проверка способности применять ИИ для решения практических задач бизнеса.
Как бы вы подошли к задаче нормализации неструктурированных клинических данных с помощью LLM или Python?
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