Senior Engineer- Machine Learning

JOB REQUIREMENTS: ESSENTIAL JOB DUTIES: Leadership & Scope-Setting Act as project lead engineer for machine learning initiatives; may lead a technical team Define the scope of machine learning projects and investigations; develop or provide input to schedules and budgets Define and control model design standards and validation criteria Mentor other engineers, including guidance on data ingestion, cleaning, and model development practices Provide oversight of model performance dashboards, ML-driven product support issues, and customer-impacting model behavior ML Pipeline & Model Lifecycle Design, build, and maintain end-to-end machine learning pipelines, from data preparation through training, validation, deployment, and production monitoring Develop, tune, and maintain predictive models, classifiers, and AI tools, such as installation quality and outage detection models, to improve accuracy and reliability Develop time series data mining and anomaly detection algorithms to monitor the health and performance of millions of deployed meters, sensors, radios, and IoT devices Recommend corrective actions and trigger field responses, or hand off findings to design and reliability engineers Establish testing and validation frameworks to confirm model outputs against real-world data, including coordinating field or manual verification studies Monitor deployed models for drift, degraded performance, or unexpected outcomes, and lead remediation efforts Support integration of model outputs into customer-facing dashboards and reporting tools Identify opportunities to improve existing infrastructure, workflows, products, and investigations with machine learning models Data Infrastructure & Integration Design and maintain data pipelines that integrate internal and external data sources, such as carrier network data, weather data, and manufacturing test data, into shared indices and datasets Interface with AWS cloud infrastructure for machine learning workloads, such as SageMaker, EC2, ECS, and Glue Utilize datastores such as Elasticsearch and Amazon Redshift, including indices and data structures supporting model training and inference Contribute to database and data architecture improvements as needed to support growing model and pipeline complexity Cross-Functional Collaboration Partner with engineering, digital engineering, and analytics teams to align model outputs with product and business needs Work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses revealed by fleet and field data Share standardized machine learning libraries, tools, and best practices across teams to reduce duplicated effort and improve consistency Collaborate with customer-facing teams to validate model results and translate findings into actionable insights Documentation & Process Initiate and manage tickets in the team's ticketing system (e.g., JIRA) Write, review, validate, and audit procedures related to model development, testing, and deployment ***** OTHER EXPERIENCE AND QUALIFICATIONS: QUALIFICATIONS: Education Bachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experience Required Experience & Technical Skills 5+ years of related experience in machine learning, data science, or applied analytics; a Master's degree with related research may count toward a portion of this experience, depending on the topic and exposure to distributed sensor and device network data Strong foundation in engineering and statistical fundamentals Proficiency in Python and SQL; experience with NoSQL data stores, Elasticsearch, Amazon Redshift, Grafana, and Jupyter Notebooks; additional languages (e.g., C#) a plus Experience with version control tools and workflows (e.g., GitHub) for code and model management Experience with cl ud-based data and machine learning platforms, preferably Amazon Web Services (e.g., S3, SageMaker, Redshift, EC2, ECS, Glue) Significant experience with statistics, machine learning, or other mathematical modeling and simulation techniques, including predictive modeling, classification, time series data mining, and anomaly detection Experience modeling performance and detecting anomalies in IoT device, sensor, or endpoint data at scale (e.g., fleets of meters, radios, or connectivity equipment) Experience with large-scale time series data sets and near-real-time analysis Experience designing, testing, validating, and deploying machine learning models in a production environment Experience with data visualization and business intelligence (BI) tools Ability to work with non-technical stakeholders to define expectations and success criteria for new models and algorithms, and to communicate results in clear, non-technical terms Preferred Qualifications Ability to quickly develop working knowledge of metering, sensor, radio, and connectivity products Ability to independently solve problems and implement solutions Demonstrated judgment and decision-making within a defined level of authority Demonstrated ability to drive projects to completion Experience integrating external or third-party data sources (e.g., carrier network data, weather data, manufacturing/test data) into machine learning pipelines Experience managing machine learning pipelines end-to-end, from training through deployment and production monitoring Understanding of device hardware, reliability, or failure analysis sufficient to translate model findings into corrective-action recommendations (e.g., using field returns or test data) Familiarity with Lean Six Sigma or other continuous improvement methodologies ***** APPLICATION INSTRUCTIONS: Apply Online: https://talentwire.org/5i6tzM