Staff Applied Scientist, LTV Modeling

1 мес. назад

СШАLead

machine learningdata analysismodelingpython

Staff Applied Scientist role focusing on modeling and optimizing long-term value (LTV) in a marketplace discovery context using machine learning.

О компании

is a technology wholesale platform built on the belief that the future is local. Independent retailers around the globe collectively represent a multi-hundred-billion-dollar wholesale market that has historically been fragmented and offline. At , we're using the power of tech, data, and machine learning to connect this thriving community of entrepreneurs across the globe. Picture your favorite boutique in town — we help them discover the best products from around the world to sell in their stores. With the right tools and insights, we believe that we can level the playing field so businesses can grow and local communities can thrive.
We’re looking for smart, resourceful and passionate people to join us as we power the shop local movement. If you believe in community, come join ours.

Обязанности

As a Staff Applied Scientist on the Discovery team, you'll own how measures and optimizes the long-term value of a discovery impression — one of the highest-leverage open problems on our marketplace. Our rankers today optimize for order conversion, helping retailers find brands and products they love. But we know our ranking algorithms can do more: helping retailers find not just products they love, but brands they can build long-lasting, successful partnerships with.
Reordering is one clear signal of this — successful brand-retailer relationships compound into substantial reorder volume over time — but not every discovery order evolves into a lasting partnership. Identifying the ones that will compound, and helping them grow, matters enormously for our community.
This is a rare opportunity to define a measurement problem from first principles. You'll build the LTV framework, design the experiments that validate it, and turn the result into a shared signal that all discovery algorithms can act on.
Own how we measure and optimize the long-term value of a discovery impression — how it contributes to the discovery of new brands retailers might love, and how it strengthens existing promising relationships so they compound.
Create the initial LTV framework: form and prioritize hypotheses about what drives long-term relationship value and the key short- vs. long-term tradeoffs, with assumptions made explicit and testable, and lay out the experimentation roadmap to validate and refine it.
Lead the implementation of v0 of the LTV model into a long-running ranking experiment, setting north star metrics as well as guardrails to maximize organizational learning, with a defined readout cadence and course-correction plan.
Deliver the long-term surrogate metric — a near-term readout predictive of long-term value — accounting for confounding factors and inherent uncertainties in measurement and marketplace dynamics.
Own the LTV model tech stack and operating standards, continuously improving the capabilities and accuracy of the model as it becomes consumable across search, reorder, and ads.

Требования

5+ years applying ML and statistical modeling to real business problems, shipping to production.
Deep causal inference expertise — quasi-experimental methods, rigorous confounder control, and healthy skepticism of analytical results.
Strong experimentation design skills, especially long-horizon experiments — surrogate/proxy metrics and variance reduction for sparse, delayed outcomes.
Baseline knowledge of search and recommendation systems on e-commerce or marketplace platforms.
Strong statistical analysis and data engineering skills — SQL/ETL and data transformation at scale.
An excitement and willingness to learn new tools and techniques.
Excellent communication skills and the ability to work in a highly cross-functional team.

Будет плюсом

PhD in CS, Stats, Economics, OR, or a related STEM field.
LTV / lifetime-value optimization on a two-sided marketplace, e-commerce platform, or other recommendation systems.
Deep learning, machine learning, or learning-to-rank techniques.

Условия

San Francisco & New York: the pay range for this role is $ 246,500 to $ 339,000 per year.
This role will also be eligible for equity and benefits. Actual base pay will be determined based on permissible factors such as transferable skills, work experience, market demands, and primary work location. The base pay range provided is subject to change and may be modified in the future.
Hybrid employees currently go into the office 3 days per week on Tuesdays, Thursdays, and a third flex day of their choosing (Monday, Wednesday, or Friday). Additionally, hybrid in-office roles will have the flexibility to work remotely up to 4 weeks per year. Specific Workplace and Information Technology positions may require onsite attendance 5 days per week as will be indicated in the job posting.

Другое

Move fast: You'll own meaningful problems that serve customers around the globe with the agency to move fast and see your results clearly.
Equipped to scale: We invest in what matters, including the latest enterprise AI tools, to help you work smarter and get more out of every day.
Best in class: Our team is full of sharp, kind, and generous colleagues who care about their craft and about helping you grow in yours.
Real rewards. Competitive pay, equity, and comprehensive benefits designed to support your life inside and outside of work.
Belonging: We're intentional about building an environment where every employee has equal access to opportunities, growth, and success.
was founded in 2017 by a team of early product and engineering leads from Square. We’re backed by some of the top investors in retail and tech including: Y Combinator, Lightspeed Venture Partners, Forerunner Ventures, Khosla Ventures, Sequoia Capital, Founders Fund, and DST Global. We have headquarters in San Francisco and Kitchener-Waterloo, and a global employee presence across offices in Toronto, London, and New York. To learn more about and our customers, you can read more on our blog .
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