Senior Data Scientist- DS& AI Org

Job Title: Data Scientist, Senior-Enterprise DS & AI Org

Job Level: Senior Over 14 Yes exp

Location: Oakland, CA

Tax Terms- W2/C2C

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
• 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
• Strong in Python & R

Department Overview

The Data Science & Artificial Intelligence Department consists of a "Delivery" team that develop data science and machine learning solutions and a "Center of Excellence" team that supports other practitioners in an enterprise-wide Hub & Spoke analytics adoption model.

As a Delivery team, this Department uses industry leading data science and change management practices to drive Client's transition to the sustainable grid of the future. The Department works cross-functionally across the company to enable data driven decisions applying analytics, as well as improvements to relevant business processes. Deployed to some of client's highest priority arenas, the Department does not specialize in a traditional utility domain, such as asset management or program administration, but instead specializes in extracting useful insights from disparate data sets and facilitating actions informed by these insights.

This team works on a wide variety of difficult problems, offering great variety in the work, and constant opportunity to explore and learn. 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 SmartMeter 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

Client 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 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)

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