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Machine Learning Scientist

Posted 18 days agoViewed

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💎 Seniority level: Middle, 4+ years

💸 Salary: 180000.0 - 270000.0 USD per year

🔍 Industry: Software Development

🏢 Company: Whatnot👥 251-500💰 $260,000,000 Series D almost 3 years agoInternetMarketplaceE-CommerceInformation TechnologyTrading PlatformCollectibles

⏳ Experience: 4+ years

Requirements:
  • Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Economics, a related technical field, or equivalent work experience.
  • 1+ years of software or machine learning engineering experience building for consumer-scale loads.
  • Industry experience with a track record of applying the scientific methods to solve real-world problems on consumer scale data.
  • Experience leading work to develop and deploy machine learning- and data-based solutions in production.
  • Extensive experience with Python and SQL for data science, machine learning, and software development e.g. PyTorch, LightGBM, FastAPI
  • Ability to work autonomously and lead initiatives across multiple product areas and communicate findings with leadership and product teams.
  • Comfortability with data warehouses and transformation tools such as Snowflake, dbt, Dagster.
  • Proficiency and experience in applied statistics and machine learning fields e.g. Experimentation and Causal Analysis, Recommendations, Fraud & Anomaly Detection, Natural Language Processing.
  • Professionalism around collaborating in a remote working environment and well tested reproducible work.
  • Above average documentation and communication skills.
Responsibilities:
  • Develop and deploy new production machine learning algorithms and systems that enrich the buyer and seller experience.
  • Build and help set direction across the entire machine learning development process to implement production algorithms, including exploratory data analysis, data modeling, feature engineering, model training, testing, deployment, and monitoring.
  • Contribute across the data science and machine learning development stack: ideation, opportunity sizing, prototyping, load testing, deployment, and monitoring.
  • Drive initiatives across multiple product areas and communicate findings with leadership and product teams using dashboards, notebooks, and/or documents where appropriate.
  • Design and implement end-to-end data pipelines and systems that support MLOps and critical business processes.
  • Deep dive into mission-critical problems unique to Whatnot’s livestream auction ecosystem and design bespoke algorithmic solutions.
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