Senior Data Scientist

5 days ago

Sunnyvale, CA, United States Macpower Digital Assets Edge Full time
Job Summary: We are seeking a highly experienced Data Scientist to join our analytics team, with a focus on advanced data modeling and AI applications in manufacturing. This role requires strong expertise in statistical modeling, machine learning, and time series analysis, coupled with a solid foundation in programming and data manipulation.

Key Responsibilities:
  • Data Analysis: Analyze large, complex datasets to uncover actionable insights for business and manufacturing operations.
  • Model Development: Design and implement machine learning models, including predictive and statistical models (e.g., regression, classification, clustering).
  • Time Series Modeling: Build and validate models using time-series data for forecasting and anomaly detection in industrial environments.
  • Visualization: Create dashboards and reports to visualize key metrics using tools such as Matplotlib and Seaborn.
  • Collaboration: Partner with cross-functional teams-engineering, product, marketing-to gather requirements and deliver high-impact data solutions.
  • Data Quality: Ensure data integrity through preprocessing, cleaning, and validation routines.
  • Reporting: Communicate findings to stakeholders through presentations and documentation.


Required Qualifications:
  • Master's or Ph.D. in Data Science, Computer Science, Statistics, or a related discipline.
  • 5+ years of experience in data science or analytics roles.
  • Proficiency in Python or R.
  • Experience with statistical/empirical model building.
  • Strong knowledge of machine learning techniques: regression, classification, clustering, neural networks.
  • Extensive experience with time series data modeling and analysis.
  • Hands-on experience with Python libraries: pandas, NumPy, SciPy.
  • Experience with SQL and relational databases.
  • Familiarity with data visualization tools such as Matplotlib and Seaborn.


Preferred Skills:
  • pplied experience in manufacturing environments or industrial analytics.
  • Experience deploying AI solutions in production systems.
  • Knowledge of big data technologies: Hadoop, Spark.
  • Cloud platform exposure (AWS, Google Cloud, Azure).
  • Familiarity with version control systems like Git.

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