Principal Data Scientist

Job Title

Pay Range: $128.66hr - $148.45hr

Requirement/Must Have

• 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 possessing Doctoral Degree or higher in a related field.

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 for machine learning feature engineering.
• Applies data science/machine learning/artificial intelligence methods to develop defensible and reproducible predictive or optimization models.
• 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 advanced data analysis.
• Works with stakeholder departments and subject matter experts to understand application and potential of data science solutions.
• Presents findings and makes recommendations to senior management.
• Acts as peer reviewer of complex models.

Nice to Have

• 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 the use of 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.
• Knowledge of industry trends and current issues in job-related area of responsibility.
• Competency with Agile product development best practices.
• Proficiency with Python or PySpark, code reviews, and code development best practices.
• Proficiency in explaining technical concepts including 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.

Skills

• Pyspark proficiency.
• User interface development proficiency.
• Strong cross-functional collaboration skills.