数据科学家，分析
Data Scientist, Analytics
职位描述
Thatch 正在重新定义健康福利的运作方式。我们构建软件，让员工真正掌控如何使用他们的福利。
我们正在寻找一名数据科学家，帮助 Thatch 在产品、商业、财务和运营决策方面做出更好的选择。你将负责问题的两个方面：使严谨分析成为可能的数据模型和度量系统，以及分析本身——调查业务问题并推荐下一步行动。这项工作涉及真实资金和真实的医疗决策，因此数据必须经得起推敲。但同时，也要让依赖这些数据的团队能够轻松使用。
这是一个动手能力强、全栈的分析岗位。根据你的经验，你可能会更专注于构建核心分析和内部工具，或者承担更广泛的度量、实验和战略决策支持职责。
你将负责的工作
- 与产品、GTM（增长型市场）、财务和运营部门的干系人合作，将模糊的问题转化为清晰的分析方法和建议。
- 分析产品、客户、转化漏斗、营销和财务数据，解释表现并发现机会。
- 通过构建可信赖的指标来衡量市场进入表现，这些指标连接归因、CRM 和产品使用数据。
- 构建和维护公司使用的 dbt 模型、指标、仪表盘和分析工具。
- 通过测试、监控、文档和更好的数据产品来提升数据质量，并实现自助式分析。
我们寻找的人选
- 有利用数据回答业务问题并影响决策的经验。
- 具备强大的 SQL 技能，并有使用 dbt 或类似数据转换工具的经验。
- 有构建分析数据模型的经验，并能通过 BI 或可视化工具传达洞察。
- 熟悉 Python，用于分析、自动化或统计建模。
- 在平衡速度、严谨性和可维护性时，具备良好的干系人合作、沟通和判断能力。
我们在不同级别招聘，最看重的是你曾负责的范围、你带来的影响，以及你如何做决策。
工具和技术栈
- 使用 dbt 和 SQL 进行建模。
- Snowflake 作为我们的数据仓库（正在从 Redshift 迁移）。
- Omni 用于 BI，搭配 Hex 等工具进行临时分析。
- GitHub 配合 CI/CD 进行版本控制。
- AI 工具（如 Claude、ChatGPT、Copilot）已融入我们的日常开发和分析流程。
你不会直接在我们的应用中工作
查看英文原文
About Thatch
For most people, health insurance is something that happens to them: a plan chosen by someone else, with little room to fit their life. Thatch is changing that. We help companies give every employee a real healthcare budget instead of a one-size-fits-all plan, so people can choose the coverage and care that works for them, and employers get predictable costs they can control.
It’s a simple idea, but making it real means solving hard problems across healthcare, payments, and technology, and we’re just getting started. We look for people who like solving complex problems, use good judgment, and are comfortable working through ambiguity. People here care deeply about our customers, take real responsibility for what gets built, and are driven by doing work that matters.
About the role
Thatch is rewiring how health benefits work. We build software that gives employees real control over how they use their benefits.
We're looking for a Data Scientist to help Thatch make better product, commercial, financial, and operational decisions. You'll own both halves of the problem: the data models and measurement systems that make rigorous analysis possible, and the analysis itself - investigating business questions and recommending what we should do next. This work touches real money and real healthcare decisions, so the numbers need to hold up. But so does making them simple to use for the teams who depend on them.
This is a hands-on, full-stack analytics role. Depending on your experience, you may focus more on building core analytics and internal tools, or take on broader ownership of measurement, experimentation, and strategic decision support.
What you will do
- Partner with stakeholders across Product, GTM, Finance, and Operations to turn ambiguous questions into clear analytical approaches and recommendations.
- Analyze product, customer, funnel, marketing, and financial data to explain performance and identify opportunities.
- Own how we measure go-to-market performance by building trusted metrics that connect attribution, CRM, and product usage data.
- Build and maintain the dbt models, metrics, dashboards, and analytical tools the company runs on.
- Improve data quality and enable self-serve analytics through testing, monitoring, documentation, and better data products.
What we are looking for
- Experience using data to answer business questions and influence decisions.
- Strong SQL skills and experience with dbt or similar data-transformation tools.
- Experience building analytical data models and communicating insights through BI or visualization tools
- Working knowledge of Python for analysis, automation, or statistical modeling.
- Strong stakeholder partnership, communication, and judgment when balancing speed, rigor, and maintainability
We hire across levels and care most about the scope you’ve owned, the impact you’ve had, and how you make decisions.
Tools and tech stack
- dbt and SQL for modeling.
- Snowflake as our data warehouse (migrating from Redshift).
- Omni for BI, with tools like Hex for ad hoc analysis.
- GitHub with CI/CD for version control.
- AI tools (e.g., Claude, ChatGPT, Copilot) are woven into our daily development and analysis workflow.
You won’t work directly in our application layer day-to-day, but understanding source data from systems like Ruby on Rails is important.
Experience that stands out
- Partnering closely with Growth, Marketing, Revenue Operations, or other GTM teams on acquisition, funnel, lifecycle, or marketing analytics.
- Designing and analyzing geo experiments or other quasi-experimental studies when randomized experiments aren't feasible.
- Building or applying statistical models such as lead scoring, forecasting, or segmentation.
- Creating internal tools or automations that improve how teams work.
- Working in healthcare, fintech, or other complex, regulated domain.
How we work
- We move quickly and are biased toward action.
- We ship early, learn from real usage, and iterate.
- We operate with trust, ownership, and high empathy.
- We value clarity over ceremony and quality over shortcuts.
What to expect
We aim to move quickly, and most candidates complete the process within 2–3 weeks. You can expect:
- An initial conversation with a recruiter.
- A conversation with the hiring manager and a lightweight Python coding exercise.
- A take-home analytics exercise.
- A review of your work, a data modeling and metrics exercise with the team, and a discussion with cross-functional partners.
- A final conversation with engineering and company leadership.
Estimated Compensation Range
$160,000—$200,000 USD