Full-Time

Product Analytics Lead

Confirmed live in the last 24 hours

Collective Hub

Collective Hub

201-500 employees

Financial services for self-employed entrepreneurs

Financial Services

Compensation Overview

$157.5k - $190kAnnually

Senior

San Francisco, CA, USA

Hybrid position in San Francisco or remote within the US.

Category
Data Science
Data Analysis
Data & Analytics
Required Skills
Python
R
SQL
Data Analysis
Requirements
  • 6+ years experience working in an analytical or product role.
  • Bachelor's degree in Mathematics, Statistics, a relevant technical field, or equivalent practical experience.
  • Experience with data querying languages (e.g. SQL), scripting languages (e.g. Python), and/or statistical/mathematical software (e.g. R).
  • Proficiency in quantitative analysis geared towards drawing actionable insights from complex datasets.
  • Experience performing exploratory analysis with minimal direction to answer ambiguous open ended questions.
  • Experimentation experience to design multivariate tests, synthesize test results and build frameworks to make data-informed launch decisions.
  • Creative product ideation skills to apply growth tactics broadly to different product areas and think through detailed elements of a product experience.
Responsibilities
  • Approach problems from first principles, synthesize large datasets into clear data visualizations + insights to tell compelling stories, get to the 'so what' on customer behavior and product initiatives.
  • Identify opportunities to drive growth and prioritize them to maximize long term growth.
  • Lead experimentation - all the way from designing tests to analyzing results in order to make launch decisions.
  • Use data to understand trends in customer behavior and product usage to influence product strategy.
  • Build, monitor, and report on metrics that drive the product strategy and facilitate decision making for key business initiatives.
  • Demonstrate a sense of urgency, attention to detail, and excellent business judgment.
  • Define, understand and interpret A/B experiments.
  • Effectively process, cleanse, and combine data sources in useful ways to curate ETL datasets that can be used by the broader team.
  • Effectively communicate your work with Product leads and XFN stakeholders regularly.

Collective Hub provides financial solutions for self-employed entrepreneurs and small business owners, focusing on helping them save money on taxes by organizing their businesses as S Corporations. Members go through a simple onboarding process to determine if this structure is beneficial, and if so, they receive personalized support from a Collective Advisor. The company sets up a tailored back office system that includes monthly bookkeeping and payroll services, along with access to a dedicated finance team. Collective Hub operates on a membership model with a tax-deductible fee, aiming to simplify financial management for its members.

Company Stage

Seed

Total Funding

$76.5M

Headquarters

San Francisco, California

Founded

2020

Growth & Insights
Headcount

6 month growth

9%

1 year growth

41%

2 year growth

23%
Simplify Jobs

Simplify's Take

What believers are saying

  • AI-powered accounting tools streamline financial management for solopreneurs, enhancing efficiency.
  • S Corporation organization helps members save significantly on taxes, attracting potential clients.
  • Access to a dedicated finance team ensures personalized advice and customer satisfaction.

What critics are saying

  • Competitors launching AI-powered tools could increase market competition for solopreneur services.
  • Activist campaigns may pose reputational risks if Collective is linked to controversial entities.

What makes Collective Hub unique

  • Collective offers a unique all-in-one financial solution for self-employed entrepreneurs.
  • The company provides personalized back office systems, including bookkeeping and payroll services.
  • Collective's membership model offers tax-deductible fees, enhancing affordability and value.

Help us improve and share your feedback! Did you find this helpful?