Senior Data Scientist Manufacturing Analytics

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:

• Applied 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.