Macpower Digital Assets Edge
Senior Data Scientist - Manufacturing Analytics
Macpower Digital Assets Edge, Sunnyvale, California, United States, 94087
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.
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.