Machine Learning Engineer - Data Science & Analytics

Machine Learning Engineer | Data Science & Analytics
Our present and future success depends on the creative and dedicated people of our company who demonstrate the principles outlined in the APS Promise: Design for Tomorrow, Empower Each Other and Succeed Together.

Are you passionate about leveraging machine learning to drive meaningful business outcomes? As a Machine Learning Engineer , you'll take ownership of the end-to-end MLOps lifecycle, building and managing scalable solutions that turn complex data into actionable insights.

What your day would be like:

You are responsible for:

MLOps Expertise: Design, build, deploy, and monitor machine learning solutions to enhance business operations in areas such as distribution assets and customer solutions.

Data-Driven Development: Create and optimize scalable, resilient systems capable of processing and analyzing large volumes of data.

Collaboration: Partner with data scientists, architects, and cross-functional teams to design innovative solutions aligned with business needs.

Technical Leadership: Conduct code reviews, mentor team members, and implement best practices for software and ML development.

Documentation & Visualization: Develop architecture diagrams, maintain robust documentation, and design workflows to monitor key performance indicators (KPIs).

The kinds of people we want to talk to have done many of the following:

Technical Prowess: Experience in machine learning, data engineering, and cloud-based solutions, with a strong understanding of scalable architectures.

Analytical Mindset: Ability to think critically and creatively to solve complex problems and deliver impactful solutions.

Team Collaboration: Strong interpersonal skills to work effectively with subject matter experts, engineers, project managers, and vendors.

Organizational Excellence: Exceptional organizational skills to manage multiple projects while maintaining attention to detail.

Communication Skills: Clear and effective communication to document processes, present insights, and foster collaboration across teams.

If you're a problem-solver with a passion for machine learning and a commitment to excellence, apply today and help us shape the future of intelligent business solutions!

BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field

AND minimum six (6) years directly related data analytics, data science, predictive modeling, building and deploying machine learning solutions

OR advanced degree and four (4) years directly related experience.

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.

High level of proficiency in commonly used programming languages and tools like Python, SQL , and cloud solutions to build and deploy scalable solutions.

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.

Preferred Special Skills, Knowledge or Qualifications

Masters or Doctorate degrees in relevant fields

2+ yrs of hands-on experience with major cloud machine learning and MLOps services in an enterprise setting.

Familiarity with PyTorch or Tensorflow.

1+ yr of experience with scaling infra using GPUs or PySpark

2+ yrs experience with MLOps services including docker, CI/CD, Kubernetes, and building/managing/monitoring pipelines.

Experience with serving generative AI services

Experience in integrating ML inferences with web services

Major Accountabilities
1) 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.
2) 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 clusters or nodes, and establish performance checks on the large data models.
• Design and implementation of solutions including data acquisition, storage, transformation, and analysis
3) 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 a 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.
4) 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.
5) Programming and Coding
• Utilize programming languages, such as R, Python, SQL, .net, Java, or C++ to evoke the data from data sources 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
6) Mentoring and coaching junior staff as necessary
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