دانشمند داده و مهندس یادگیری ماشین نگهداری پیش‌بینانه

Master's or Ph.D. in Mechanical Engineering, Electrical Engineering, Computer Science/Engineering, or a related field.

Proven experience in Predictive Maintenance and Condition Monitoring with a focus on Signal Processing techniques such as FFT, Short Time Fourier Transform, Time Synchronous Averaging, Wavelet Transform, Spectral Kurtosis, Spectral Correlation, Envelope Analysis, Hilbert Transform, and related methods.

Strong foundations in Machine Learning and model development, including classical Machine Learning algorithms, supervised and unsupervised learning, anomaly detection, feature engineering, Artificial Neural Networks, Convolutional Neural Networks, Sequence-based Models such as RNNs and LSTMs, and Attention Mechanisms.

Hands-on experience architecting and delivering production ML solutions on AWS is required, including several of: SageMaker (Training, Processing, Pipelines, Model Registry, Endpoints), S3, Athena, AWS Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, IoT Core, CloudWatch, and IAM.

Demonstrated data and ML engineering at scale, including distributed processing with Spark (PySpark) or equivalent, efficient handling of large time-series and binary signal data, and pragmatic reasoning about compute cost, runtime, and reprocessing risk.

Experience designing data mining and annotation workflows in partnership with domain experts, including label taxonomy design, labeling tooling, label quality control, and converting noisy expert feedback into model-ready ground truth.

Experience using Large Language Models, AI-assisted coding, and agentic AI tools for software and ML development; familiarity with designing or integrating LLM- or agent-based workflows for data processing, analysis, decision support, or automation is highly desirable.

Physics-informed ML and experience with additional modalities (electrical/current signature analysis, ultrasound, oil analysis) are a plus.

Extract, preprocess, and analyze data from various sensor sources, primarily machine vibration, while also working with temperature, electrical, image, and machine-context data to identify and enhance diagnostic features and fault-specific signatures.

Train, validate, and deploy predictive maintenance models to accurately identify and predict machinery faults such as bearing, gearbox, shaft, motor, pump, fan, and belt faults. This may include CNNs, RNNs, LSTMs, attention-based models, and other time-series or representation-learning approaches.

Develop and evaluate time-domain, frequency-domain, and contextual features, including harmonics, sidebands, envelope-spectrum features, running-speed characteristics, bearing and gear-mesh fault frequencies, and other fault-specific evidence.

Act as the solution architect for assigned ML initiatives: define the end-to-end design — data sources and contracts, batch vs. near-real-time execution, feature computation strategy, storage layout (S3 partitioning, Timestream, Aurora/MySQL), orchestration, inference pattern, and cost/scalability tradeoffs — and document the design before large-scale execution, since backfills across tens of thousands of assets are expensive to rerun.

Design and run data mining and annotation workflows that connect Condition Monitoring Engineers with Data Science — including case discovery, labeling interfaces and conventions, fault-type and time-window labeling, label QA, and turning expert feedback into reusable supervised training data.

Design experiments and compare new algorithms against existing production methods, investigating false positives, false negatives, suppression behavior, data-quality issues, and performance across different machines, operating conditions, sampling configurations, and sensor characteristics.

Work closely with MLOps and Data Engineering teams to ensure smooth deployment of models, feature pipelines, data contracts, and other ML solutions in production environments, including CI/CD for ML, model versioning and registry, IaC (CloudFormation/CDK/Terraform), containerization (Docker/ECR), and production monitoring and alerting via CloudWatch, and participate in production validation and troubleshooting.