Overview
Skills
Job Details
Data Scientist, Senior Enterprise DS & AI Org
Remote (work PST time zone)
Senior Over 15 Yes exp required
Only W2
Key points
Ability to synthesize complex information into clear insights and translate those insights into decisions and actions. Demonstrated ability to explain in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
Competency in the mathematical and statistical fields that underpin data science
Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies
Strong in Python & R
Current and past engagements include:
- Creating wildfire risk models that are used by regulators and the utility to prioritize asset management
- Developing computer vision models that improve, accelerate, and automate asset inspections processes
- Predicting electric distribution equipment failure before it occurs, allowing for proactive maintenance
- Forming the analytical framework behind client s Transmission Public Safety Power Shutoff
- Optimizing non-wires alternative resource portfolios, like the Oakland Clean Energy Initiative, including location and resource adequacy considerations
- Analyzing customer demographic, program participation, and Smart Meter interval data to build program targeted propensity models, e.g. for customer owned distributed energy resource technologies
- Identifying and investigating anomalous customer natural gas usage, in order to resolve dangerous customer side leaks
Position Summary
PG&E is looking for a Data Scientist with experience in delivering data science products end-to-end. In this role, the successful candidates will be uniquely positioned at the forefront of utility industry analytics, having the opportunity to advance Client s triple bottom line of People, Planet, and Prosperity. Working as part of cross functional teams, including data engineers, machine learning engineers, data scientists, and subject matter experts, this individual will lead the development of computer vision models to improve, accelerate, and automate asset inspections processes. The individual will participate in the full lifecycle of the delivery process from initial value discovery to model-building to building data products to deliver value to end users.
The responsibilities of these positions include:
Leads conversations with business stakeholders and subject matter experts to understand business and subject matter context
Scopes and prioritizes modeling work to deliver business value
Applies data science, machine learning and other analytical modeling methods to develop defensible and reproducible predictive models
Serves as the technical lead for the development of computer vision models, leading data labeling, model training and model evaluation
Extracts, transforms, and loads data from dissimilar sources from across PG&E for model-building and analysis
Writes and documents python code for data science (feature engineering and machine learning modeling) independently
Documents and presents data science experiments and findings clearly to other data scientists and business stakeholders.
Act as peer reviewer of models and analyses built by other data scientists
Develops and presents summary presentations to business.
Present findings and makes recommendations to officers and cross-functional management.
Build and maintain strong relationships with business units and external agencies.
Works with cross functional teams, including data engineers, machine learning engineers, data scientists, and subject matter experts
Education Minimum: Bachelor s degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.
Education Desired: Master s degree in one of the above areas.
Experience Minimum: 4 years in data science (or 2 years, if possess master s degree, as described above).
Knowledge, Skills, Abilities and (Technical) Competencies:
- Demonstrated knowledge of and abilities with data science standards and processes (model evaluation, optimization, feature engineering, etc.) along with best practices to implement them
- Competency in software engineering, statistics, and machine learning techniques as they apply to data science deployment
- Competency in commonly used data science and/or operations research programming languages, packages, and tools.
- Hands-on and theoretical experience of data science/machine learning models and algorithms
- Ability to synthesize complex information into clear insights and translate those insights into decisions and actions.
Demonstrated ability to explain in breadth and depth technical concepts including but not limited to statistical inference, machine learning algorithms, software engineering, model deployment pipelines.
- Competency in the mathematical and statistical fields that underpin data science
- Mastery in systems thinking and structuring complex problems
- Ability to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologies
- Desired: experience building computer vision models
- Desired: experience with AWS technologies (S3, GroundTruth, Sagemaker)