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About This Role
ABOUT QUINCE
Founded in 2018, Quince was built to challenge the idea that nice things have to cost a lot. Our mission is simple: to make really high quality essentials for really low prices, produced fairly and sustainably. We believe everyone deserves exceptional craftsmanship and timeless design without the traditional markups. Quince is a direct\-to\-consumer (DTC) model that cuts out middlemen and leverages just\-in\-time manufacturing to minimize waste and maximize value.
Quince is a tech company disrupting the retail industry by putting AI, analytics and automation at the center of everything we do. Our unwavering commitment to excellence and company values guide our teams and actions:
- Customer First: We prioritize customer satisfaction in every decision.
- High Quality: True quality means premium materials and rigorous production standards you can feel good about.
- Essential Design: We focus on timeless, functional essentials instead of chasing trends.
- Always a Better Deal: Innovation and transparency ensure value for both customers and partners.
- Social \& Environmental Responsibility: We commit to sustainable materials, ethical production, and fair wages.
Quince partners with world\-class manufacturers across the globe and serves millions of customers. With strong investor backing and a focus on sustainable growth, we are a company that is rapidly scaling while maintaining a commitment to quality, simplicity, and radical price transparency.
OUR TEAM AND SUCCESS
At Quince, you will be part of a high\-performing team that is redefining what quality, value, and sustainability mean in modern retail. We are a destination for builders, innovators, and operators to come together and challenge the status quo. Our collective ambition is bold. We are creating an entirely new category and customer experience – one that democratizes luxury and provides high quality products at radically low prices. That mission demands a world\-class team committed to excellence.
If you are motivated by impact, growth, and purpose, you will find a strong sense of belonging at Quince.
THE ROLE
Data Scientist, Growth
We are seeking a Data Scientist, Growth to join our growing team. In this high\-exposure role, you will own the machine\-driven decision engines and automated bidding infrastructure guiding a massive global performance marketing spend across 40\+ business units. You will have the 0\-to\-1 autonomy to build end\-to\-end production models and write the core software fueling our growth engine. Blending rigorous applied research with immediate technical execution, this position offers a direct line to executive leadership and a clear, measurable view of your impact on our top and bottom lines.
### Responsibilities
- Work on the core automated bidding infrastructure and allocation algorithms to model where the next advertising dollar yields maximum return.
- Build end\-to\-end production machine learning models from 0 to 1 with immediate, high\-visibility impact on the company's top line.
- Design, build, and productionalize autonomous models and production\-level data pipelines that automate operational business logic and spend efficiency.
- Manage ad platform integrations, handle bidding automation, and optimize complex marketing tech workflows.
- Work on automated processes for design and creative production
- Function as a deep, independent individual contributor digging deep into complex systems and tackling loosely defined optimization and statistical problems.
- Collaborate cross\-functionally with marketing, product, and data science teams while maintaining high\-visibility access to executive leadership.
### Qualifications
Required
- 2\+ years of technical experience, including at least 1 year of corporate/industry experience.
- Technical proficiency in Python and SQL.
- Proven background as a Data Scientist, Machine Learning Engineer, or ML Researcher with hands\-on experience independently pushing machine learning models into a live production environment.
- Deep analytical and problem\-solving strengths with a demonstrated history of working independently on loosely defined optimization and statistical problems.
- Excellent collaboration and communication skills to work effectively with cross\-functional partners and leadership teams.
- Ability to dig deep into complex systems within a fast\-paced, high\-leverage entrepreneurial environment without a massive supporting infrastructure.
Preferred
- Direct domain experience in ad tech infrastructure, automated campaign management, digital advertising, performance marketing tech, programmatic bidding optimization or automated investment platforms.
- Experience working within a direct\-to\-consumer, retail, or e\-commerce environment, particularly owning data pipelines for real\-time bid pricing or user attribution platforms.
- Practical engineering exposure to large language models (such as Claude) and working with vector spaces.
WHY QUINCE?
Joining Quince means being part of a mission\-driven team reshaping retail. You will work alongside talented colleagues, tackle meaningful challenges, and contribute to building a more sustainable, accessible future for customers and partners alike.
EQUAL OPPORTUNITY \& HIRING INTEGRITY
Quince provides equal employment opportunities to all employees and applications for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran or military status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.
Quince is committed to providing reasonable accommodations to qualified individuals with disabilities. If you need a reasonable accommodation to complete your application or to perform the essential functions of a role at Quince, please let us know by completing this accommodation form. We review all requests individually and will work with you to determine appropriate accommodations on a case\-by\-case basis.
Employment is contingent upon successful completion of a background check. Quince will conduct background checks in compliance with applicable federal, state, and local laws.
*Security Advisory: Beware of Frauds*
At Quince, we're dedicated to recruiting top talent who share our drive for innovation. To safeguard candidates, Quince emphasizes legitimate recruitment practices. Initial communication is primarily via official Quince email addresses and LinkedIn; beware of deviations. Personal data and sensitive information will not be solicited during the application phase. Interviews are conducted via phone, in person, or through the approved platforms Google Meets or Zoom—never via messaging apps or other calling services. Offers are merit\-based, communicated verbally, and followed up in writing. If personal information is requested to initiate the hiring process, rest assured it will be through secure and protected means.
Salary Context
This $171K-$198K range is above the 75th percentile for Data Scientist roles in our dataset (median: $155K across 226 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Quince, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. Disclosed range: $171K to $198K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Quince AI Hiring
Quince has 1 open AI role right now. They're hiring across Data Scientist. Based in Palo Alto, CA, US. Compensation range: $198K - $198K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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