Senior Engineer- Machine Learning

Badger Meter - Where Every Drop Counts and So Do YouAt Badger Meter, we're more than a leading global water technology company - we're innovators with a mission: to preserve and protect the world's most precious resource. For over 120 years, our trusted solutions have enabled our customers to optimize the delivery and use of water, maximize revenue and reduce waste. Every employee at Badger Meter is an important part of our success. Here, your work doesn't just move a business forward - it shapes a more sustainable future. We are committed to building a workplace where we celebrate differences, empower voices, and encourage fresh ideas that drive innovation. When you join us, you'll find:Purpose-driven work that makes a real difference in communities around the globe.Career growth and development opportunities designed to help you achieve your potential.A supportive, inclusiveculture where collaboration and creativity thrive. Be part of something bigger. At Badger Meter, your contributions will ripple far beyond the workplace - creating lasting change for people and the planet.What You Will Contribute:Senior Engineer, Machine LearningJob DescriptionThe Senior Engineer, Machine Learning is a mid-career position of independence and ownership of large investigations and projects. The Senior Engineer will define the scope and tasks for machine learning initiatives and may perform the efforts themselves or with one or more engineers assisting. The successful candidate will possess strong knowledge of modeling techniques and system performance, and be able to define resources and time needed to estimate project efforts. This role focuses on the design, development, testing, validation, deployment, and ongoing tuning of machine learning pipelines and models that support product performance, installation quality, and outage detection, as well as fleet-wide health monitoring of the deployed population of millions of meters, sensors, radios, connectivity equipment, and other IoT devices in support of the Systems Integration team, and also encompasses the data infrastructure, integration, and monitoring systems that keep those models reliable at scale. The Senior Engineer is expected to contribute quickly to the team's portfolio of algorithms, ML models, and AI tools, creating predictive models, classifiers, time series data mining, and anomaly detection algorithms, and will work closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses. The Senior Engineer will represent the team on projects and investigations, report findings to leadership, and act as a mentor to other engineers.ESSENTIAL JOB DUTIES:Leadership & Scope-SettingAct as project lead engineer for machine learning initiatives; may lead a technical teamDefine the scope of machine learning projects and investigations; develop or provide input to schedules and budgetsDefine and control model design standards and validation criteriaMentor other engineers, including guidance on data ingestion, cleaning, and model development practicesProvide oversight of model performance dashboards, ML-driven product support issues, and customer-impacting model behaviorML Pipeline & Model LifecycleDesign, build, and maintain end-to-end machine learning pipelines, from data preparation through training, validation, deployment, and production monitoringDevelop, tune, and maintain predictive models, classifiers, and AI tools, such as installation quality and outage detection models, to improve accuracy and reliabilityDevelop time series data mining and anomaly detection algorithms to monitor the health and performance of millions of deployed meters, sensors, radios, and IoT devicesRecommend corrective actions and trigger field responses, or hand off findings to design and reliability engineersEstablish testing and validation frameworks to confirm model outputs against real-world data, including coordinating field or manual verification studiesMonitor deployed models for drift, degraded performance, or unexpected outcomes, and lead remediation effortsSupport integration of model outputs into customer-facing dashboards and reporting toolsIdentify opportunities to improve existing infrastructure, workflows, products, and investigations with machine learning modelsData Infrastructure & IntegrationDesign 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 datasetsInterface with AWS cloud infrastructure for machine learning workloads, such as SageMaker, EC2, ECS, and GlueUtilize datastores such as Elasticsearch and Amazon Redshift, including indices and data structures supporting model training and inferenceContribute to database and data architecture improvements as needed to support growing model and pipeline complexityCross-Functional CollaborationPartner with engineering, digital engineering, and analytics teams to align model outputs with product and business needsWork closely with developers, data engineers, data scientists, and reliability, quality, and design engineering to identify and address product weaknesses revealed by fleet and field dataShare standardized machine learning libraries, tools, and best practices across teams to reduce duplicated effort and improve consistencyCollaborate with customer-facing teams to validate model results and translate findings into actionable insightsDocumentation & ProcessInitiate and manage tickets in the team's ticketing system (e.g., JIRA)Write, review, validate, and audit procedures related to model development, testing, and deploymentQUALIFICATIONS:EducationBachelor's or Master's degree in Engineering, Mathematics, Statistics, Computer Science, Data Science, or Data Analytics, or equivalent practical experienceRequired Experience & Technical Skills5+ 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 dataStrong foundation in engineering and statistical fundamentalsProficiency in Python and SQL; experience with NoSQL data stores, Elasticsearch, Amazon Redshift, Grafana, and Jupyter Notebooks; additional languages (e.g., C#) a plusExperience with version control tools and workflows (e.g., GitHub) for code and model managementExperience with cloud-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 detectionExperience 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 analysisExperience designing, testing, validating, and deploying machine learning models in a production environmentExperience with data visualization and business intelligence (BI) toolsAbility 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 termsPreferred QualificationsAbility to quickly develop working knowledge of metering, sensor, radio, and connectivity productsAbility to independently solve problems and implement solutionsDemonstrated judgment and decision-making within a defined level of authorityDemonstrated ability to drive projects to completionExperience integrating external or third-party data sources (e.g., carrier network data, weather data, manufacturing/test data) into machine learning pipelinesExperience managing machine learning pipelines end-to-end, from training through deployment and production monitoringUnderstanding 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 methodologiesCompetitive Total Rewards at Badger Meter:Competitive PayAnnual Bonus Eligible for Annual Pay Increases Comprehensive Health, Vision, and Dental Coverage 15 days Paid Time Off + 11 Paid Holidays Two Ways to Save for Retirement: Badger Meter contributes 25 cents for every dollar you contribute to the plan, up to 7% of your eligible compensation. In addition to the match, the company will also contribute 5% of your eligible compensation to your Defined Contribution account on an annual basis. Additional access to a certified financial planner to help ensure your money is working for you, at no cost!Employer Paid benefits including: Employee Assistance Program (EAP), Basic Group Life Insurance, Short Term Disability, and more Educational Assistance – Tuition Reimbursement up to $5,250 Voluntary benefits including: Additional Life Insurance, Long Term Disability, Accident and Critical Illness coverageHealth Savings Account (HSA) & Flexible Spending Account (FSA) optionsAn Equal Opportunity/Affirmative Action Employer. This company considers candidates regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, disability or veteran status. Applicants can learn more about their rights regarding equal opportunity in employment by viewing the federal "EEO is the Law" poster and the “EEO is the Law” poster supplement at Badger Meter complies with all aspects of the Americans with Disabilities Act (ADA), as amended by the ADA Amendments Act, and all applicable state or local disability laws. This means that we will reasonably accommodate qualified employees with a disability if accommodation would allow them to perform the essential functions of their job, unless doing so would create an undue hardship. Privacy StatementThe Employee and Applicant Privacy Statement describes how we collect, use, share, retain, and safeguard applicant information. Please see the privacy statement on our website here. SummaryLocation: Milwaukee, WIType: Full time