At Niagara, we’re looking for Team Members who want to be part of achieving our mission to provide our customers the highest quality most affordable bottled water.
Consider applying here, if you want to:
- Work in an entrepreneurial and dynamic environment with a chance to make an impact.
- Develop lasting relationships with great people.
- Have the opportunity to build a satisfying career.
We offer competitive compensation and benefits packages for our Team Members.
As an AI/Machine Learning Engineer, you'll work on training, evaluating, and serving large AI models, internet-scale dataset building, and prototype new research and product ideas. Your responsibilities include pre-training, optimizing inference throughput, continual learning, implementing interfaces, designing, testing, and optimizing new neural net architectures, and internet-scale data scraping. Additionally, you will work with Product Management and data scientists to build and constantly lead excellence in our products.
- Develop & Design Predictive analytic systems through Continuous Monitoring of critical asset parameters
- Data Analysis: Conduct in-depth analysis of large and complex datasets related to equipment sensor data, maintenance logs, and other relevant sources using machine learning programs and libraries such as Python with libraries like NumPy, Pandas, and SciPy.
- Model Development: Design and implement advanced predictive maintenance models using machine learning algorithms and techniques from libraries such as scikit-learn, TensorFlow, or PyTorch. Apply regression, classification, clustering, and time series analysis algorithms to develop accurate predictive models.
- Feature Engineering: Utilize machine learning libraries to extract, transform, and engineer relevant features from raw data. Use feature selection techniques and data preprocessing methods available in libraries like scikit-learn to optimize model performance.
- Model Training and Evaluation: Utilize machine learning frameworks to train and fine-tune predictive maintenance models using historical data. Evaluate model performance using metrics such as accuracy, precision, recall, and F1-score, leveraging libraries like scikit-learn or Keras.
- Data Visualization and Reporting: Utilize data visualization libraries like Matplotlib or Plotly to create interactive visualizations and reports that effectively communicate insights derived from predictive maintenance models.
- Collaboration and Cross-functional Communication: Collaborate with maintenance engineers, data engineers, domain experts, and stakeholders from different departments, using tools like Jupyter Notebooks or Git, to share code, insights, and results. Foster effective communication and knowledge sharing within the team.
- Data Governance and Quality Assurance: Ensure data integrity, quality, and security throughout the predictive maintenance process. Implement data cleansing, validation, and quality control measures using libraries like Pandas or PySpark to ensure accurate and reliable model outputs.
- Research and Innovation: Stay updated with the latest advancements in machine learning and predictive maintenance. Explore new machine learning algorithms, libraries, and techniques that can enhance predictive maintenance capabilities.
- Documentation: Maintain clear and concise documentation of methodologies, code implementations, and model specifications using tools like Markdown or Sphinx. Document experiments, findings, and best practices for future reference and knowledge sharing.
- Continuous Improvement: Continuously explore ways to improve model performance, scalability, and efficiency using machine learning libraries and frameworks. Keep up with the latest research papers and developments to incorporate cutting-edge techniques into predictive maintenance models.
- Training and Knowledge Transfer: Share expertise and insights with colleagues, stakeholders, and other team members. Conduct training sessions or workshops to promote understanding and effective utilization of machine learning programs and libraries.
- Develop and design advanced systems for asset reliability for all manufacturing assets across all Niagara plants.
- Machine Learning Pipeline Development (ML Ops): Design, develop, and implement end-to-end machine learning pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment. Implement automation and orchestration techniques to ensure reproducibility and scalability.
- Data Analysis and Modeling: Apply advanced statistical analysis and machine learning techniques to analyze large, complex datasets. Develop predictive models, classification algorithms, and optimization algorithms to solve business problems and generate actionable insights.
- Data Cleaning and Preprocessing: Clean, transform, and preprocess data to ensure data quality and suitability for analysis. Handle missing data, outliers, and noise to ensure accurate modeling results.
- Feature Engineering: Identify relevant features and variables from raw data and create new features to enhance model performance. Conduct feature selection and extraction techniques to improve model accuracy and interpretability.
- Model Development and Evaluation: Develop and implement predictive models using machine learning algorithms such as regression, decision trees, random forests, neural networks, or deep learning. Evaluate model performance, conduct hypothesis testing, and fine-tune models to optimize accuracy and generalizability.
- Data Visualization and Communication: Visualize and communicate complex data analysis and model results to non-technical stakeholders effectively. Prepare clear and concise reports, dashboards, and presentations to convey insights and recommendations.
- Collaborative Problem Solving: Collaborate with cross-functional teams including data engineers, business analysts, and domain experts to understand business challenges and develop data-driven solutions. Participate in brainstorming sessions and provide technical expertise to drive innovative problem-solving approaches.
- Experimental Design and A/B Testing: Design and execute experiments to test hypotheses, measure the impact of interventions, and optimize business outcomes. Conduct A/B testing and analyze experimental results to provide insights and recommendations for improvement.
- Continuous Integration and Deployment: Implement CI/CD (Continuous Integration/Continuous Deployment) practices to automate the testing, integration, and deployment of machine learning models. Establish version control processes for models and associated code.
- Security and Compliance: Ensure the security and compliance of machine learning systems, including data privacy, access controls, and regulatory requirements. Implement secure storage and transmission of sensitive data.
- Performance Optimization: Optimize the performance and efficiency of machine learning models and infrastructure, including resource allocation, parallel processing, and distributed computing. Identify and resolve bottlenecks and scalability challenges.
- Documentation and Knowledge Sharing: Document the design, implementation, and maintenance processes of machine learning pipelines and infrastructure. Share knowledge and best practices with the team and stakeholders to foster a culture of learning and improvement.
- Systems Reliability Engineer is estimated to travel 10-20%
- Please note this job description is not a full list of activities, duties, or responsibilities required of the employee for this job. Duties, responsibilities, and activities may change at any time with or without prior notice.
- 2-4 years – Experience in Industrial ML/Automation/Data Science or other related fields
- 2-4 years – Experience in a position in the Industrial ML/Automation/Data Science field in manufacturing
- Experience may include a combination of work experience and education
- 3-5 years – Experience in Industrial ML/Automation/Data Science or other related fields
- 3-5 years – Experience in a position in the Industrial ML/Automation/Data Science field in manufacturing
- Experience may include a combination of work experience and education
Preferred Competencies and Skills
- 3+ years of industry experience in developing and productionizing applied machine learning for solving business problems
- Proficiency in Azure ML Studio, AWS and related tools for model development, deployment, and monitoring.
- Expertise and experience in both supervised and unsupervised learning
- Expertise and experience in neural networks or reinforcement learning
- Expertise and experience in modern NLP, large language models, or generative AI
- Proficiency with Python
- Proficiency in using query languages such as SQL, Hive, Pig. Etc.
- Experience working with machine learning frameworks such as TensorFlow, PyTorch, Spark ML, scikit-learn, or related frameworks
- Preferred experience with common data science toolkits, such as R, Weka, Python with focus on NumPy, Matplotlib and Pandas, MATLAB, etc.
- Curious, passion for learning, self-motivated, and excited about solving open-ended challenges.
- The ability to explain complex findings and technical approaches to a variety of audiences
- Desire to work with amazing, passionate all kinds of partners who care about solving challenging problems to improve productivity for its users
- Proficiency in, but not limited to:
- Microsoft Office Applications – Word, Excel, PowerPoint, Outlook, Project, Visio, etc.
- Project Management tools
- Able to translate data into recommendable actions to senior management
- Strong analytical and problem-solving skills
- Able to work with minimal supervision
- Detail-oriented with excellent oral and written communication skills
- Able to execute tasks in a very dynamic and ever-changing environment
- Minimum Required:
- Bachelor's Degree in Computer/Industrial/Automation/Data Science Engineering or other related fields or equivalent experience
- Master's Degree in Computer/Industrial/Automation/Data Science Engineering
Typical Compensation Range
Pay Rate Type: Salary
$95,301.00 - $138,186.00 / Yearly
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