AMISEQ
Senior Data Scientist
AMISEQ, Oakland, California, United States, 94601
Job Description: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 Clients triple bottom line of People, Planet, and Prosperity. Working as part of cross functional teams, including data engineers, data scientists, technologists, subject matter experts, and change management professionals ? 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 contextScopes and prioritizes modeling work to deliver business valueApplies 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 evaluationExtracts, transforms, and loads data from dissimilar sources from across for model-building and analysisWrites and documents python code for data science (feature engineering and machine learning modeling) independentlyDocuments 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 scientistsDevelops 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:Bachelors degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.Education Desired: Masters degree in one of the above areas.Experience Minimum: 4 years in data science for business (or 2 years, if possess masters 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 themCompetency in software engineering, statistics, and machine learning techniques as they apply to data science deploymentCompetency 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 algorithmsAbility 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 scienceMastery in systems thinking and structuring complex problemsAbility to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologiesDesired: experience building computer vision models ? Desired: experience with AWS technologies (S3, GroundTruth, Sagemaker)
The responsibilities of these positions include:Leads conversations with business stakeholders and subject matter experts to understand business and subject matter contextScopes and prioritizes modeling work to deliver business valueApplies 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 evaluationExtracts, transforms, and loads data from dissimilar sources from across for model-building and analysisWrites and documents python code for data science (feature engineering and machine learning modeling) independentlyDocuments 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 scientistsDevelops 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:Bachelors degree in Data Science, Machine Learning, Computer Science, Physics, Econometrics or Economics, Engineering, Mathematics, Applied Sciences, Statistics, or equivalent field.Education Desired: Masters degree in one of the above areas.Experience Minimum: 4 years in data science for business (or 2 years, if possess masters 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 themCompetency in software engineering, statistics, and machine learning techniques as they apply to data science deploymentCompetency 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 algorithmsAbility 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 scienceMastery in systems thinking and structuring complex problemsAbility to develop, coach and teach career level data scientists in data science/artificial intelligence/machine learning techniques and technologiesDesired: experience building computer vision models ? Desired: experience with AWS technologies (S3, GroundTruth, Sagemaker)