Principal Applied Scientist - Remote USA (*eligible states)
About the position
The Principal Applied Scientist at The RealReal will play a crucial role in enhancing the Pricing team's objectives by leading applied science projects that tackle complex business challenges related to pricing strategies. This position involves collaboration with Product and Engineering teams to create technical roadmaps and deliver machine learning solutions that significantly impact revenue generation and model deployment efficiency. The role is centered around applying advanced machine learning techniques to optimize pricing and discounting processes, contributing to a sustainable future for fashion.
Responsibilities
Lead the design, development, and deployment of machine learning models to solve strategic business problems.
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Conduct deep analyses on complex datasets to derive actionable insights using advanced methodologies.
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Develop and maintain clean, efficient, and scalable code that meets industry standards.
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Collaborate with cross-functional teams to ensure alignment of machine learning solutions with business goals.
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Provide mentorship to junior and mid-level ML engineers, fostering expertise in pricing-related ML domains.
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Contribute to the development of technical roadmaps and product initiatives.
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Influence technical direction and take ownership of key components within the pricing ecosystem.
Requirements
10+ years of industry experience in applied Machine Learning, including a proven track record in designing, deploying, and scaling production-level ML models.
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Master's or PhD in AI, Computer Science, Econometrics, Mathematics, Statistics, Electrical Engineering or related field.
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8+ years experience in building, deploying, and managing machine learning models in production environments at scale, focusing on pricing and discounting.
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Extensive knowledge of ML best practices and advanced ML algorithms/techniques.
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Experience in at least one of these domains: price optimization, discounting, algorithmic bidding.
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Extensive experience in scientific and ML libraries in Python and deep learning frameworks.
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Strong data engineering skills and experience working with large scale datasets.
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Hands-on experience with big data tools for distributed processing of large datasets.
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Proficiency with cloud platforms for scalable model deployment.
Nice-to-haves
PhD in Computer Science, Machine Learning, Econometrics, AI or related field.
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Strong background in applying Machine Learning techniques to solve real-world business problems in retail or e-commerce.
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Hands-on experience with MLOps tools and pipelines.
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Impact-focused mindset, with a commitment to delivering high-quality, business-oriented ML solutions.
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Demonstrated leadership and mentoring skills.
Benefits
Paid parental leave
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Employee stock purchase plan
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Paid holidays
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Health insurance
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Dental insurance
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Vision insurance
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401(k) matching
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