

Data Scientist with a Ph.D.
β - Featured Role | Apply direct with Data Freelance Hub
This role is a 6-month remote contract for a Data Scientist with a Ph.D. requiring 2+ years of experience in data science, spatio-temporal statistics, and proficiency in Python or R. Relevant utility industry experience is desired. W2 candidates only.
π - Country
United States
π± - Currency
$ USD
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π° - Day rate
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ποΈ - Date discovered
June 24, 2025
π - Project duration
Unknown
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ποΈ - Location type
Remote
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π - Contract type
W2 Contractor
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π - Security clearance
Unknown
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π - Location detailed
San Francisco, CA
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π§ - Skills detailed
#Spatial Data #AI (Artificial Intelligence) #AWS (Amazon Web Services) #R #ML (Machine Learning) #Statistics #Time Series #Python #Cloud #Computer Science #Azure #GCP (Google Cloud Platform) #Consulting #Programming #"ETL (Extract #Transform #Load)" #Data Science #Mathematics #Datasets #Data Analysis
Role description
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6-month contract position for a Data Scientist with a Ph.D. on a remote basis
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β’ We can only consider candidates willing to work on W2 basis
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β’ Position Summary
Our 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 clientβs triple bottom line of People, Planet, and Prosperity.
Department Overview:
The Data Science & Artificial Intelligence Department consists of a βDeliveryβ team that develop data science and machine learning solutions.
β’ As a Delivery team, this Department uses industry leading data science and change management practices to drive clientβs transition to the sustainable grid of the future.
β’ The Department works cross-functionally across the company to enable data driven decisions applying analytics, as well as improvements to relevant business processes.
β’ Deployed to some of clientβs 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 datasets and facilitating actions informed by these insights.
Responsibilities:
β’ Design and develop production-quality scientific algorithms in Python to extract patterns of customer energy consumption, as well as other customersβ characteristics and attributes.
β’ Develop spatio-temporal algorithms to predict adoption of Electric Vehicles by customers.
β’ Perform in-depth validation of our algorithms that is driven by business and technical requirements.
β’ Perform deep root-cause analysis, EDA, and error analysis of the ML models.
β’ Collaborate with members of your team and with domain experts (e.g., Power Distribution, Grid Planning, Clean Energy Transportation) to understand practical implications of your model, to collect business requirements and to deliver results to business partners.
β’ Communicate technical information, their implications and applications to peers, various business partners, and strategic leaders across the company.
Requirements:
β’ Ph.D. in Engineering, Computer Science, Physics, Econometrics or Economics, Mathematics, Applied Sciences, Statistics, or other highly quantitative discipline.
β’ Demonstrated knowledge of and abilities with data science standards and processes (model building and evaluation, optimization, feature engineering, etc.) along with best practices to implement them.
β’ Experience with spatio-temporal statistics, geospatial data analysis, and machine learning techniques for time series with large datasets.
β’ 2+ years of experience writing software to extract features from time series data or large-scale datasets.
β’ Proficiency in programming languages such as Python and/or R.
β’ Experience designing, developing, and maintaining scientific code that runs at scale.
β’ Excellent problem solving and communication skills.
β’ Strong understanding of applied statistics and probability.
Desired Qualification:
β’ Experience with handling large datasets and cloud computing platforms (e.g. AWS, Azure, GCP, or other enterprise level analytics platforms.
β’ Experience turning business needs into technical requirements, and structuring developments, analysis and validation plans to meet requirements.
β’ Relevant industry experience (electric or gas utility, EV charging infrastructure, Vehicle-Grid integration, distributed energy resources, analytics consulting, etc.).