Data Scientist / Data Scientist, Senior

APS•Phoenix, AZ

1d•Remote

About The Position

Are you a Data Scientist / Data Scientist, Senior ready to make a big impact at scale? We're looking for a highly skilled Data Scientist / Data Scientist, Senior to lead the design and deployment of production-grade machine learning systems in a complex enterprise environment. You’ll own the full MLOps lifecycle—from prototyping to monitoring—and architect solutions that power intelligent, real-time decision-making across critical business functions. This is a high-visibility role where you’ll collaborate with cross-functional teams, influence architecture, and help define best practices that shape the future of ML at scale.

Requirements

Held a Machine Learning Engineer or MLOps role in a large-scale enterprise environment.
Deep experience with modern ML models, cloud-native data platforms, and orchestration tools (e.g., Kubeflow, SageMaker, MLflow).
Proven ability to design scalable ML architectures for streaming and batch use cases.
A mindset for mentorship and technical leadership, with the ability to guide teams on best practices in production ML.
BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field PLUS minimum four (4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role OR advanced degree and two (2) years directly related experience (for Data Scientist).
BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field PLUS minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role OR advanced degree and four (4) years directly related experience (for Data Scientist, Senior).
Possesses a combination of strong analytical and problem-solving skills and programming knowledge, or an equivalent combination of education and experience with demonstrated comparable knowledge and abilities.

Nice To Haves

Masters or Doctorate degrees in related fields.
Knowledge/experience in utility industry and business functions.
Certification in Data Science and/or predictive analytics.
A high level of proficiency in commonly used programming languages and tools like R Programming, Python and SQL.
Strong communication, presentation and writing skills.
Must be able to lead teams in evaluations and implementation of solutions.
Must be able to work with key internal and external stakeholders and all levels of management.

Responsibilities

Lead MLOps Initiatives: Design, build, deploy, and monitor end-to-end ML solutions that are scalable, reliable, and secure.
Architect for Scale & Speed: Build applications optimized for low latency on high-volume data pipelines and streaming environments.
Advise & Innovate: Act as a thought partner to data scientists and engineering leaders, bringing deep domain expertise in ML model design and infrastructure.
Collaborate Cross-Functionally: Work with enterprise architects, product teams, and data scientists to deliver real-world business value.
Own Quality & Governance: Establish and maintain best practices for ML lifecycle management, including CI/CD, monitoring, testing, and documentation.
Collaboration with customers and partners: Consult with stakeholders and subject matter experts to understand business needs and operations, goals and objectives and key drivers for performance.
Work closely with the business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners and vendors.
Data requirements and preparation: Identify available and relevant data and the data sources.
Collaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.
Gather data, formulate cluster or nodes and establish performance checks on the large data models.
Design and implementation of solutions including data acquisition, storage, transformation, and analysis.
Modeling and Deployment: Design, develop and deploy innovative models. Provide insights from predictive statistical modeling activities. Test theories by creating models and experimenting with data.
Design models, algorithms and visualizations that help distill insights from huge volumes of chaotic data.
Modeling complex problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and visualization techniques using existing or new front-end reporting & analytics tools.
Play key role in turning data into critical information and knowledge that can be used to make sound organizational decisions.
Propose innovative ways to look at problems by using data mining approaches and validate findings using experimental and iterative approaches.
Understand data transforming platforms and technologies and maintain a knowledge of discipline maturity.
Present results, provide recommendations and lead analytics efforts: Present findings to the business in a way that can be easily understood by business counterparts.
Make recommendations based on business requirements and knowledge of industry best practices.
Make technical decisions on advanced analytics initiatives.
Programming and Coding: Utilize programming language, such as R, Python, SQL, .net, Java or C++ to evoke the data from data source and model.
Familiarity with Cloud structure and building, utilizing cloud technologies.
Performing data acquisition using JSon, SQL, ODBC, JScript, or API for Big Data extracts.
Transform and utilize streaming data with programming languages such as: KAFKA, SQL, Spark, and/or Azure.
Mentoring and coaching junior staff as necessary.