Part Time Data Scientist Risk Jobs (NOW HIRING)

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

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

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.

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.

• 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.

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.

• 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.