Senior Applied AI/ML Scientist - Brand Growth
Faire is hiring a Senior Applied AI/ML Scientist - Brand Growth in San Francisco, United States. It pays $211k-$291k a year and Level rates it ; you can apply on Faire.
Level's read on this job
- Builds AI ●●●● is how Level rates this role: the job is building AI systems.
- Tools and skills the posting cites include NLP.
- Level classifies it as a senior role.
- Level lists 67 other open roles at Faire.
AI in this role
About Faire
Faire 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 Faire, 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.
About this role
Faire leverages the power of machine learning and data insights to revolutionize the wholesale industry, enabling local retailers to compete against giants like Amazon and big box stores. Our highly skilled team of Applied AI/ML Scientists specialize in developing algorithmic solutions for notification and recommender systems, advertising attribution, and LTV predictions. We are dedicated to building machine learning models that help our customers thrive.
As a Senior Applied AI/ML Scientist on the Brand Growth team, you will own the modeling and measurement for a set of problems that help Faire acquire and grow the brands on our marketplace, which connects hundreds of thousands of independent brands and retailers. Because our users are businesses, there is a wealth of information about them that ML models can use to personalize their experience, grow their success, and prioritize where we invest. You will work across structured and unstructured data using methods that range from personalization and recommendations to causal inference, lifetime-value modeling, and information extraction. You will partner closely with product, engineering, marketing, and sales, and you will drive projects end-to-end from framing through production and measurement.
Our team already includes experienced Applied AI/ML Scientists from Uber, Airbnb, Square, Facebook, and Pinterest. Faire will soon be known as a top destination for data scientists and machine learning engineers, and you will help take us there!
What you’ll do
- Own applied ML projects end-to-end: framing the problem, building and shipping the model, and measuring impact.
- Optimize marketing and acquisition spend for acquiring new brands through targeting, lookalike audiences, and personalization.
- Predict brand lifetime value to prioritize brand leads for sales and to personalize new-brand onboarding.
- Improve cold-start recommendations and exploration so that new brands find the right retailer audience quickly.
- Identify and enrich brand leads from internal and external data (e.g. retailer search behavior, referrals, and third-party sources) to power prioritization and personalization.
- Use experimentation and causal inference to measure the effectiveness of growth and spend levers.
- Partner across product, engineering, marketing, sales, and analytics to turn models into shipped product and business impact.
- Solve challenging problems related to a two-sided marketplace.
Qualifications
- 3+ years of industry experience using machine learning to solve real-world problems
- Experience with relevant business problems (e-commerce)
- Experience with relevant technical methods (LTV modeling, NLP, LLMs, causal ML, bidding optimization)
- Strong programming skills
- An excitement and willingness to learn new tools and techniques
- The ability to design and implement ML solutions without supervision
- Strong communication skills and the ability to work in a highly cross-functional team
Great to Haves
- Highly recommended: Master's or PhD in Computer Science, Statistics, or related STEM fields.
- Previous experience in marketplace growth, acquisition / paid marketing, LTV modeling, or personalization for a two-sided platform.
Salary Range
San Francisco: the pay range for this role is $211,000 to $290,500 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.
Faire uses Artificial Intelligence (AI) to screen and select applicants for this position.
This job posting is for an existing vacancy.
Hybrid Faire 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.
Why you’ll love working at Faire
- Flexible paid time off, plus company-wide recharge days
- Paid parental leave up to 16 weeks
- Comprehensive health coverage, with options varying by location
- Fertility and family-building benefits through Carrot, including a lifetime reimbursement allowance
- Mental health support and coaching through Modern Health
- Stipends to support your professional development, wellness, and work from home needs
- Employer-sponsored retirement plan, dependent on country of employment
Faire 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 Faire and our customers, you can read more on our blog.
Faire provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, genetics, sexual orientation, gender identity or gender expression.
Faire is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Accommodations are available throughout the recruitment process and applicants with a disability may request to be accommodated throughout the recruitment process. We will work with all applicants to accommodate their individual accessibility needs. To request reasonable accommodation, please fill out our Accommodation Request Form (https://bit.ly/faire-form)
Privacy
For information about the type of personal data Faire collects from applicants, as well as your choices regarding the data collected about you, please visit Faire’s Privacy Notice (https://www.faire.com/privacy)
How we rate this
Senior Applied AI/ML Scientist - Brand Growth at Faire rates 87 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
Adapt your resume
- List these exact terms on your resume: NLP. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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