Senior Data Scientist - Predictive Modeling & AI Lead

Spectraforce Technologies; Oakland (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Spectraforce Technologies seeks a Principal Data Scientist to lead design, development, and deployment of ML/AI models using structured and unstructured data from multiple sources. You will develop predictive or optimization models and educate non-technical stakeholders on model maturity, risks, and value.

The role emphasizes strong collaboration with cross-functional teams, hands-on data wrangling, and building reusable data science components, with a focus on electric/utility domain

Qualifications

• Master's Degree in Data Science, ML, CS, or related field.
• Experience in Data Science: 8 years or 2 years if Doctoral degree or higher.

Responsibilities

• Researches and applies advanced data science principles to inform business decisions.
• Creates data mining architectures, models, protocols, and data analysis methodologies.
• Extracts, transforms, and loads data from disparate sources for ML feature engineering.
• Applies ML/AI methods to develop defensible predictive or optimization models.
• Wrangles and prepares data for model development and feature engineering.
• Architects, develops, and documents reusable data science functions and modular code.
• Assesses business implications of modeling decisions and analytic processes.
• Collaborates with stakeholders to understand application and value of data science solutions.
• Presents findings to senior management and acts as peer reviewer of complex models.

Skills

Pyspark

UI Development

Cross-functional Collaboration

Education

Master's Degree

Doctorate (PhD)

Tools

Python

PySpark

Foundry

AWS

Job description

Spectraforce Technologies seeks a Principal Data Scientist to lead design, development, and deployment of ML/AI models using structured and unstructured data from multiple sources. You will develop predictive or optimization models and educate non-technical stakeholders on model maturity, risks, and value.

The role emphasizes strong collaboration with cross-functional teams, hands-on data wrangling, and building reusable data science components, with a focus on electric/utility domain