Principal Data Scientist

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

Principal Data Scientist

12 months+ contract

Oakland, CA-Hybrid (one day per week onsite)

****Local Candidates Only****

Equipment: Client's laptop will be provided upon start (or within a few days). If delayed, personal device may be used via Citrix/VDI

Top Skills

• Pyspark Proficiency
• User Interface Development Proficiency
• Strong Cross-Functional Collaboration Skills

Qualifications

Minimum:

• Master's Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
• Experience in Data Science, 8 years or 2 years experience, if possess Doctoral Degree or higher in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.

Desired:

• Doctorate Degree in Data Science, Machine Learning, Computer Science, Civil Engineering, Mechanical Engineering, Electrical Engineering, Statistics, or equivalent field.
• Expertise in experimental design and causal inference methods.
• Expertise in statistical methods for time series analysis, statistical modeling, and probabilistic risk assessment.
• Relevant industry experience (electric or gas utility, data science consulting, etc.)
• Familiarity with supervised, unsupervised, deep learning & physics-based methods for modeling electrical infrastructure failure modes.
• Competency with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them.
• Knowledge of industry trends and current issues in job-related area of responsibility as demonstrated through peer reviewed journal publications, conference presentations, open source contributions or similar activities.
• Competency with Agile product development best practices.
• Proficiency with Python or Pyspark, code reviews, and code development best practices.
• Proficiency in explaining breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
• Mastery in clearly communicating complex technical details and insights to colleagues and stakeholders.
• Ability to develop, coach, teach and/or mentor others to meet both their career goals and the organization goals.

Position Summary

Leads the design, development, and execution of scripts, programs, models, user interfaces, algorithms, and processes, using structured and unstructured data from disparate sources and sizes, generating defensible, valid, scalable, reproducible and documented machine learning and artificial intelligence models (predictive or optimization) for problem solving and strategy development. Educates the non-technical community on advantages, risks, and maturity levels of data science solutions.

Job Responsibilities

• Researches and applies advanced knowledge of existing and emerging data science principles, theories, and techniques to inform business decisions.
• Creates advanced data mining architectures / models / protocols, statistical reporting, and data analysis methodologies to identify trends in structured and unstructured data sets.
• Extracts, transforms, and loads data from dissimilar sources from across client for their machine learning feature engineering.
• Applies data science/machine learning/artificial intelligence methods to develop defensible and reproducible predictive or optimization models that involve multiple facets and iterations in algorithm development.
• Wrangles and prepares data as input for machine learning model development and feature engineering.
• Architects, develops, and documents reusable functions and modular code for data science.
• Assesses business implications associated with modeling assumptions, inputs, methodologies, technical implementation, analytic procedures and processes, and advanced data analysis.
• Works with stakeholder departments and company subject matter experts to understand application and potential of data science solutions that create value.
• Presents findings and makes recommendations to senior management.
• Acts as peer reviewer of complex models.

Department Overview

The aim of the Undergrounding Risk Management team in the Undergrounding & System Hardening organization is to enhance the risk practices of client's Electric Operation business and thereby address changing external conditions such as climate change. To this end the Electric Risk Management & Analytics team develops, maintains, and applies predictive models to close the gap between metrics and electric system performance. These models provide a multi-layered view of risk and risk reduction across the electric system so that decision-making processes include and empower employees at all levels of the company to manage risk appropriately.

Sample Activities

• Quantification of wildfire mitigation program performance on the distribution and transmission electric system.
• Development of predictive models using Python or PySpark and executed in Foundry or AWS.
• Interpretation and representation of meteorological data in models that combine a range of data sources such as the electric system asset data, vegetation, and meteorology.
• Designing statistical methodology and architecting programmatic solutions to utilize risk model outputs for business use cases.