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Senior Applied AI/ML Scientist - Brand Growth
Faire · San Francisco, CA
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What Faire asks for
From Faire's Senior Applied AI/ML Scientist - Brand Growth ad, checked 2 Nov 2026.
End-to-end ML deployment
Owning the full life cycle of machine learning projects, from defining the problem to building models, deploying them to production, and evaluating their business impact.
E-commerce and LTV modeling
Applying machine learning to e-commerce challenges, specifically predicting customer lifetime value and optimizing marketing spend for new user acquisition.
Causal ML and experimentation
Using experimental design and causal inference methods to determine how specific growth levers and marketing spends actually change business outcomes.
Cross-functional partnership
Collaborating with product, engineering, and sales teams to translate machine learning models into live product features and business results.
The ad asks for 3 or more years of industry machine learning experience and a hybrid schedule in San Francisco. A Master's or PhD in a STEM field is highly recommended.
How the example answers the ad
End-to-end ML deployment
“Owned the end-to-end development of a real-time LTV prediction model that increased user retention by 14 percent over 12 months.”
Ownership of the full development cycle for an LTV model used in real-time shows end-to-end machine learning impact. The 14 percent increase in retention measures the business results.
Causal ML and experimentation
“Designed and deployed causal inference frameworks to measure the incremental impact of 50 million dollars in annual marketing spend.”
Deploying frameworks to measure the impact of 50 million dollars in spend shows causal machine learning and experimentation. It addresses how specific marketing levers change outcomes.
Cross-functional partnership
“Built a cross-functional partnership with product leadership to implement automated bidding strategies for secondary marketplace participants.”
Partnering with product leadership to implement bidding strategies shows cross-functional partnership. It translates technical models into a live marketplace feature.
E-commerce and LTV modeling
“Engineered a regression model to forecast buyer intent, driving a 10 percent increase in personalized email engagement.”
Using regression to forecast buyer intent at eBay shows e-commerce and LTV modeling. The 10 percent increase in email engagement shows the application of machine learning to user acquisition challenges.
The whole example as text
Elena Volkov · Senior Applied Scientist
Summary
Senior Applied Scientist with 6 years of experience in high-growth marketplace environments. Leads end-to-end machine learning initiatives from initial problem framing to production deployment. Develops customer lifetime value models and causal inference frameworks to optimize marketing spend across e-commerce platforms. Partners with cross-functional product and engineering teams to integrate complex NLP lead enrichment tools into production workflows.
Experience
Senior Applied Scientist · Amazon
Seattle, WA · 2023 - Present
- Owned the end-to-end development of a real-time LTV prediction model that increased user retention by 14 percent over 12 months.
- Designed and deployed causal inference frameworks to measure the incremental impact of 50 million dollars in annual marketing spend.
- Built a cross-functional partnership with product leadership to implement automated bidding strategies for secondary marketplace participants.
- Mentored a team of 4 junior scientists in model deployment best practices and scalable infrastructure architecture.
Applied Scientist · Wayfair
Boston, MA · 2022 - 2023
- Developed and launched a latent dirichlet allocation model for product categorization that improved site search relevance by 22 percent.
- Orchestrated A/B testing protocols to validate pricing experiments, resulting in a 5 percent uplift in conversion rates for wholesale inventory.
- Integrated NLP-based enrichment tools into sales workflows, reducing lead processing time by 30 percent across three regional teams.
- Collaborated with data engineering to productionize feature stores, reducing model latency by 200 milliseconds.
Data Scientist · eBay
San Jose, CA · 2020 - 2022
- Engineered a regression model to forecast buyer intent, driving a 10 percent increase in personalized email engagement.
- Executed multi-variate experiments to determine optimal delivery windows, improving customer satisfaction scores by 8 points.
- Collaborated with the supply chain team to optimize inventory forecasting using time-series analysis on 2 million transaction records.
- Automated data pipeline monitoring, reducing downtime for critical prediction services by 40 percent.
Machine Learning Research Intern · Microsoft
Redmond, WA · 2020 - 2020
- Designed a novel clustering algorithm to categorize user search queries, enhancing recommendation accuracy by 6 percent.
- Participated in the full lifecycle of a pilot project to improve query expansion using transformer-based models.
- Collaborated with research scientists to write and publish a paper on attention mechanisms for high-dimensional data.
Education
PhD in Statistics · University of Washington
Seattle, WA · 2015 - 2020
- Focused dissertation on Bayesian non-parametric methods for marketplace experimentation.
- Awarded Graduate Research Fellowship for excellence in statistical modeling.
- Maintained 4.0 GPA across core curriculum in advanced machine learning and econometrics.
BS in Mathematics and Computer Science · Carnegie Mellon University
Pittsburgh, PA · 2011 - 2015
- Graduated with College Honors and distinction in mathematics.
- Completed thesis on stochastic optimization algorithms for resource allocation.
- Served as a teaching assistant for graduate-level probability and statistics courses.
Skills
Frequently asked questions
Is the example from an employee?
No. It is an AI-written example for this ad, made to show how a resume can answer it.
Is this a remote role?
No. The role is hybrid, requiring three days per week in the San Francisco office. It offers the flexibility to work remotely for up to 4 weeks each year.
What level of education is required?
The ad lists a Master's or PhD in Computer Science, Statistics, or a related STEM field as highly recommended, but not strictly required.
Where do I apply?
On the official Faire posting, through the job link on this page.
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