Description

Job Details Great company looking to add a Senior Data Scientist to their team! This Jobot Job is hosted by: Jasmine Robinson Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume. Salary: $140,000 - $220,000 per year A bit about us: Out client provides a broad range of specialized oilfield services and equipment primarily to independent and major oil and gas companies engaged in the exploration, production and development of oil and gas properties throughout the United States. Why join us? 100% Remote (But MUST live in Texas, Oklahoma, Louisiana, Arkansas, Florida, Pennsylvania, Georgia, or Kansas) Other locations will not be considered Great culture and team Greenfield environment Job Details The Data Scientist is responsible for supporting predictive analytic initiatives that empower business decisions. This role applies industry-leading methodologies for working with large datasets to extract meaningful business insight and creatively solve business problems. This role will apply advanced methods and algorithms for identifying trends, predicting outcomes, and alerting the business to potential issues. Additionally, the Data Scientist is expected to present insights and recommendations to non-technical audiences and explain the benefits and impacts of the recommended solutions. The Data Scientist will be a key member of the Data and Analytics Platform Services team. This role will create analytical models and datasets while working with a Data Engineer to develop code for extracting data from source systems, which will include the Relational Enterprise Data Warehouse, Operational Data Store, and Azure Data Lake Store. The ideal candidate will also be passionate about developing machine learning models using Azure Databricks and/or Azure ML Studio, or a comparable platform for operationalizing Machine Learning workloads. Responsibilities: Engage with business partners and stakeholders to understand business problems and translate them into data science solutions. Coordinate and collaborate with data science, data engineering, analytic engineering, and other resources to achieve business goals. Work cross-functionally with finance, operations, and field engineering teams on opportunities for improved insights. Lead and contribute to the end-to-end development and deployment of predictive and prescriptive models. Explore large datasets using modeling, analysis, and visualization techniques. Communicate results, analyses, and methodologies to technical and non-technical senior level stakeholders. Ability to mentor, coach, and lead others. Contribute to and help build ML/AI vision to support business strategy. Required Knowledge, Skills, Abilities (Qualifications): Degree in Data Science, Machine Learning, Applied Mathematics/Statistics, or a related field. 3-5 years of experience applying data science, AI/machine learning, and analytics techniques to business problems. Experience leading data science projects. Experience with supervised and unsupervised machine modeling techniques, with a focus on time-series forecasting. Experience solving real-world problems using programming languages such as SQL, Spark, and Python, and deploying solutions to enterprise systems. Excellent strategic thinking, communication, collaboration, and problem-solving skills, including working with and articulating results to senior business stakeholders. Preferred Knowledge, Skills, Abilities: It is considered a plus to have knowledge and working experience in any of the following areas: Oil and Gas use cases for predictive analytics.

Role and Responsibilities

• Responsibilities: Engage with business partners and stakeholders to understand business problems and translate them into data science solutions. Coordinate and collaborate with data science, data engineering, analytic engineering, and other resources to achieve business goals. Work cross-functionally with finance, operations, and field engineering teams on opportunities for improved insights. Lead and contribute to the end-to-end development and deployment of predictive and prescriptive models. Explore large datasets using modeling, analysis, and visualization techniques. Communicate results, analyses, and methodologies to technical and non-technical senior level stakeholders. Ability to mentor, coach, and lead others. Contribute to and help build ML/AI vision to support business strategy.