About Mighty:
Mighty is using technology to solve one of the most important and overlooked challenges of our time: the steep obstacles faced by accident victims and the people supporting them to achieve medical recovery, financial security, and legal justice. We reject society’s stigma around personal injury and see a service that, when done well, delivers justice for millions of ordinary people who could not otherwise afford it. Fresh off our 2021 Series B from a top-tier VC, we’re building innovative solutions at the intersection of legal, financial, and health tech for the injured and everyone who supports them.

As a Senior Data Scientist at Mighty, you will be establishing our analytics function. Data plays a crucial role in Mighty’s opportunity to reshape the personal injury industry, and we have a unique and vast data set to leverage. You will be expected to drive strategic conversations with stakeholders about what we should measure and why as well as what decisions we should make and why. This role can be based our of our offices in New York, NY, Atlanta, GA, or be fully remote (US Only). 

Your Mandate

  • Identify any and all opportunities to better leverage data to inform strategy.
  • Build and maintain core reporting for the company.
  • Deliver analyses needed to make better decisions in marketing, product development, and business strategy.
  • Set the foundation for a scaled data operation with more (future) specialization in each domain (e.g. marketing).
  • Own our data stack. We are currently using Fivetran for ingestion, dbt for Transformations, and BigQuery as our data warehouse; and we are open to trying new things. The most important component of the stack that we expect for you to own and lead is the transformation layer as it serves a foundational role in the architecture of our analytical capabilities.

Your Experience

  • 4+ years working with business and product data to inform decision-making
  • A deep interest in how our business works, how decisions get made, and how data could help improve the quality of those decisions
  • Experience using the tools and techniques of “modern data stacks”
    • Dashboarding and business intelligence (e.g. Looker, Tableau, Mode, etc.)
    • Data warehousing (e.g. BigQuery, Snowflake, RedShift, etc.)
    • Transformation and modeling (e.g. dbt) 
  • Fluency in SQL
  • Ideally you have comfort with at least one programming language such as Python or R

Onboarding Plan

  • In the first 30 days you will learn our business and how we work. You will familiarize yourself with our data sets and our data stack.
  • In the first 60 days you will dive into discovery with stakeholders to begin to identify key metrics. Also, in this period, you will take ownership of our data stack and begin committing code in dbt as well as extending existing dashboards.
  • In the first 90 days you will have a long term vision and plan for how to leverage data at Mighty.

Hiring Process

The hiring process is a two way street! We want to find someone for whom Mighty is the perfect fit. Please ask us lots of questions to ensure you are learning what you need to learn about us. Here is an overview of how and what we will be assessing throughout the process:

  1. A conversation with hiring manager, VP of Engineering, Aaron Gibralter.
  2. A simple take home test to assess basic technical skills.
  3. A conversation with our recruiting team to go over some basic HR questions.
  4. A conversation with Andrew Bartholomew, who is advising us on building out our analytics function.
  5. “On-site”
    1. A case study interview to assess data exploration and visualization.
    2. A data modeling and programming interview with an engineer.
    3. A stakeholder alignment interview to assess cross-functional communication.
    4. A conversation with our founder, Josh Schwadron.

Please note: we have this role posted under a few different titles as there is not universal alignment on titles in the data world. In the interest of transparency, here is how we think about the nuances of titles with regard to this role:

  • Analytics Engineer: this best captures a need for technical rigor; however, it may not clearly stress the importance of finding someone with a very strong business sense.
  • Data Analyst: this is perhaps the most “accurate,” but it may not articulate the technical requirements; we also fear there may be a stigma against this title as “data scientist” may appeal to more people.
  • Data Scientist: this is most likely resonant with the market, but will likely generate the most noise as it has become a pretty large bucket of different skills and interests.

This article, written by Andrew Bartholomew, summarizes how we are thinking about this role.


  • Competitive compensation
  • Full Benefits - health, vision, and dental, 401(k) with company contribution
  • Flexible paid time off - take time when you need it
  • Learning and Development opportunities
  • And more!

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