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Staff Scientist

Posted 15 days agoViewed

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💎 Seniority level: Staff, 10+ Years of professional experience

📍 Location: United States of America

🔍 Industry: Automotive

🏢 Company: careers_gm

🗣️ Languages: English

⏳ Experience: 10+ Years of professional experience

🪄 Skills: LeadershipPythonJavaMachine LearningTableauAzureData scienceAngularPostgresReactSparkTensorflowMentoringData visualizationData analytics

Requirements:
  • 10+ Years of professional experience in the area.
  • Bachelor's degree in computer science, engineering, statistics, mathematics, physics, econometrics, or related degree.
  • Master’s degree in computer science, engineering, statistics, mathematics, physics, econometrics, or related degree.
  • Expert knowledge and experience in areas such as numerical optimization, econometric modeling, machine learning, Bayesian statistics, etc.
  • Experience analyzing source system data, working with structured and unstructured data.
  • Manipulating high-volume, high-dimensionality data to identify patterns, anomalies, relationships, and trends.
  • Technical knowledge in analytic tools and languages (Python, R, etc.), deep learning frameworks (TensorFlow), cloud platforms (Databricks, Azure), data management (Postgres), and data visualization tools (Power BI, Tableau).
Responsibilities:
  • Collaborate with business stakeholders, product managers, and data science teams to develop actionable, high-impact data analytical models and insights.
  • Lead and mentor other scientists to drive high-quality analytical products in a timely manner.
  • Direct the working team’s approach, balancing delivery quality, speed-to-market, and scalability.
  • Develop leading solutions through problem framing, data requirements identification, data exploration, robust model selection, and productionization.
  • Identify opportunities for data utilization and design data science solutions supporting business decisions.
  • Ensure consistent progress towards solution development and business adoption through collaboration.
  • Communicate findings and recommendations clearly to influence senior leaders, promoting data-driven decision-making.
  • Stay current with advancements in data science, AI, and machine learning, mentoring team members.
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