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Staff Data Scientist

Lily AI is the product attributes platform that injects the language of the customer across the existing retail stack, accurately connecting shoppers with the relevant products they're looking to buy. We drive 8-9 figure revenue uplift for retailers and brands by dramatically improving their on-site search conversion, relevant product recommendations, SEO/SEM, item set-up, and demand forecasting.

We use robust Computer Vision and NLP technologies to analyze a retailer's product catalog, then inject customer-centric attributes across the breadth of their existing retail stack to drive immediate relevance and connect those products with the buying objectives of customers. 7 of the top 25 fashion retailers in the world already work with Lily AI to deliver millions in top- and bottom-line revenue impact

The company is a female-founded startup headquartered in Mountain View, CA, and is backed by Canaan Partners, Conductive Ventures, Sorenson Capital, NEA, Fernbrook Management, and Unshackled Ventures, among others.

Please note the following states we are currently hiring from:
  • California
  • Florida
  • Georgia
  • Illinois
  • Massachusetts
  • Minnesota
  • Nevada
  • New Jersey
  • New York
  • North Carolina
  • Rhode Island
  • Tennessee
  • Texas
  • Virginia
  • Washington
  • Utah
  • Arizona

Why is this exciting to you?

  • Utilize quantitative analysis techniques to analyze large data sets and generate meaningful insights to inform decision-making.
  • Collaborate with various internal teams, including Product and Engineering, to implement standard experimentation methodologies and designs.
  • Continuously evaluate our experimentation platform to ensure that we measure the appropriate business metrics, apply statistical best practices, and achieve positive business outcomes.
  • Conduct a comprehensive analysis of metrics lift using causal-inference methods and innovative approaches, and communicate your findings to stakeholders and senior management.
  • Develop statistical models to gain insight into customer behavior and the factors that impact experimentation results.
  • Foster a spirit of curiosity, and strive to identify emerging trends and insights to help us enhance the customer experience and grow the business.

What we are looking for:

  • 4+ years of relevant work experience as a Data Scientist.
  • Masters or Ph.D. degree in a quantitative field such as Mathematics, Statistics, Engineering, Computer Science, or a related discipline.
  • Strong understanding of statistical techniques, such as regression models, significance testing, power calculations, and causal inference. Advanced training in statistics is preferred.
  • Proven experience designing metrics and creating systems to generate actionable business insights.
  • Proficiency in SQL and at least one scripting language, such as Python or R.
  • Familiarity with data visualization tools like Tableau, Superset, or similar platforms.
  • Exceptional attention to detail and analytical skills, with the ability to detect and diagnose anomalies, errors, or issues.
  • Excellent communication and presentation abilities, capable of effectively conveying complex quantitative analyses to engineering or executive-level stakeholders in a clear and concise manner.
  • Experience with eCommerce Analytics or AB Testing platforms is a plus.
  • Experience in data engineering is a plus.

Compensation:

Compensation is competitive and will be determined based on a combination of experience, seniority, internal, external equity and location. For some context: this position in the US would pay between $140,000 - $210,000 USD per year, depending on experience and seniority. In other regions, compensation will be adjusted for local currency and local market rates. Lily AI compensation policy is calculated with a focus on equity and where employees can thrive.

Lily AI Glassdoor Company Review
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Lily AI DE&I Review
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CEO of Lily AI
Lily AI CEO photo
Purva Gupta
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Our mission is to provide a depth and scale of attribution that no other solution can match, and turn qualitative product attributes into a universal, customer-centric mathematical language at a high volume with unprecedented accuracy. This helps ...

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DATE POSTED
April 14, 2023

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