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Data Scientist

Posted 2 days agoViewed

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💎 Seniority level: Middle, 4+ years

📍 Location: US

🏢 Company: G2👥 501-1000💰 $157,000,000 Series D almost 4 years ago🫂 Last layoff over 4 years agoConsumer ReviewsMarketplaceBusiness IntelligenceB2BEnterprise SoftwareMarketing AutomationSoftware

⏳ Experience: 4+ years

🪄 Skills: AWSDockerPythonSQLData AnalysisData MiningFlaskKubeflowMachine LearningMLFlowNumpySnowflakeAPI testingSparkTensorflowTroubleshootingDebugging

Requirements:
  • 4+ years experience as a data scientist involved in data extraction, analysis and modeling.
  • 4+ years of experience in Python and SQL
  • Strong understanding of statistics
  • Proficiency in machine learning algorithms and all stages of machine learning.
  • Familiarity with neural networks and deep learning.
  • Familiarity with AWS services and Snowflake (or similar SQL DB)
  • Familiar with containerization (e.g., Docker) and API frameworks (e.g., Flask).
  • Demonstrated ability to troubleshoot issues in production environments, including debugging data pipelines or model related errors.
Responsibilities:
  • Lead the development and refinement of machine learning models, including feature engineering, algorithm selection, and model optimization.
  • Conduct experiments with advanced machine learning techniques to improve model performance and deliver impactful solutions.
  • Build, maintain, and optimize data pipelines to support end-to-end machine learning workflows.
  • Analyze large datasets to extract insights and provide actionable recommendations for business teams in conjunction with model development
  • Collaborate with ML engineers to operationalize models, ensuring scalability and reliability.
  • Work closely with cross-functional teams, including product managers and engineers, to translate business requirements into machine learning solutions.
  • Document and present methodologies, findings, and results to both technical and non-technical audiences.
  • Act as an on-call resource to troubleshoot and resolve issues with deployed machine learning models.
  • Collaborate with ML engineers to monitor model performance and ensure operational stability.
  • Mentor junior team members, providing technical support, guidance on model development, and best practices implementation.
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