Senior Data Scientist, Acquisition and Lifecycle
About Truebill: 🔮
Truebill is the personal financial assistant you always wanted! With a mission to improve the financial health of millions, we are transforming the way people manage their expenses and grow their net worth. We save our users money and help them gain control over their finances by offering budgeting and saving tools, cancelling unnecessary subscriptions, and negotiating lower bills.
This past fall, we closed our Series B round of funding with $15 million, and now we are looking to hire a Senior Data Scientist as we continue to scale our team in the Washington, D.C. area.
About the role: 🤹♀️
Truebill is a YC backed startup based in the DC area right on the Silver Spring metro! Hundreds of thousands of people use the Truebill app to manage their daily finances and take control of their money. We just recently announced our $15m Series B round of funding and are now looking to scale our team in the Washington, DC area!
With a mission to improve the financial health of millions, Truebill is transforming the way people manage their expenses and grow their net worth. Through helping people cancel unnecessary subscriptions, negotiating bills, and securing refunds, we save our members money while helping them regain control over their finances.
About You: 🦄
The ideal candidate will partner with the Marketing team to scale our acquisition efforts and understand and improve user retention with our lifecycle team. The candidate will have ideally spent several years at the intersection of marketing, finance and product working with multiple stakeholders to optimize and report spend, growth and engagement through the lens of company KPIS and domain specific levers. They look to join an organization that has an established user base and welcome the challenges associated with scaling and retention in the consumer finance industry.
Key Role Objectives
- Working across departmental stakeholders, own the production and evolution of key company KPIs like LTV, ROAS, CAC and churn.
- Partner with the acquisition team to analyze marketing efficacy, build instrumentation that enables us to optimize our marketing efficacy across channels and construct novel models from first, second and third party data that enable us to better predict customer value at the time of acquisition.
- Support our lifecycle team to analyze our corpus of user and product data to define key activation and engagement metrics. Feeding those measures into LTV and churn models and partnering in the design, execution and analysis of experiments that target key KPIs.
- Leverage our proprietary first party dataset to build enriched metadata and modeled features of our users that serve as inputs to our optimization models and provide our company a higher fidelity view on the usage of our products.
- Develop and instrument a framework for analyzing and optimizing the success of our partnerships with reporting and targeting capabilities.
- Working in conjunction with our partners in marketing, finance and product - be a good partner in the experimentation process from hypothesis design and supporting data, to audience selection through execution and analysis.
- Reading your audience and adapting - you’ll be delivering your analyses to both founders and first day employees.
- Storytelling - being able to supplement the what with the why.
- Curiosity and a desire to solve hard problems in an imperfect world.
- Stakeholder management and prioritization.
- Thriving at the intersection of technical and business problems.
- Able to thrive in autonomy but backed by a solid, supportive data team.
- 4+ years of experience at the intersection of marketing, growth and product with practical experience in both optimization and experiment design. Experience with paid acquisition channels on at least one of the major platforms (Facebook, Google, Linkedin) and experience building multi channel attribution models is an asset.
- Demonstrated experience constructing and deploying scalable machine learning and decision systems with an end consumer focus.
- Expertise in experimental design - from inception and mining to support hypothesis generation through test design, execution and analysis.
- Practical experience mining high dimensional transaction data, synthesizing and visualizing the data at varying levels - from expert stakeholders to investors.
- A polished communicator - comfortable presenting complex topics to both technical and non-technical audiences and tailoring communications - written, verbal and presentations.
- Experience managing internal and external stakeholders and expertise dealing with third party vendors and retrieving data from external sources.
- Desire to teach and mentor members of the data team in your area of expertise.
- An advanced degree in a quantitative discipline is nice to have.