Remote Environmental Data Scientist Jobs (NOW HIRING)

MatchPoint Solutions is a fast-growing, young, energetic global IT-Engineering services company with clients across the US. We provide technology solutions to various clients like Uber, Robinhood, Netflix, Airbnb, Google, Sephora, and more! More recently, we have expanded to working internationally in Canada, China, Ireland, UK, Brazil, and India. Through our culture of innovation, we inspire, build, and deliver business results, from idea to outcome. We keep our clients on the cutting edge of the latest technologies and provide solutions by using industry-specific best practices and expertise.

We are excited to be continuously expanding our team. If you are interested in this position, please send over your updated resume. We look forward to hearing from you!

Job Title: Data Scientist

Duration: 12+ Months

Location: Remote

Rate: $75-$80/hr on W2

Key points:

Developing computer vision models that improve, accelerate, and automate asset inspections processes

Strong Python programing

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 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 the 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 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

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 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
• 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)