Remote Senior Data Scientist - Industrial IoT & Predictive Maintenance

Role Information:

• Job Title: Sr. Data Scientist
  - Industrial Industry Focused
• Work Location: Fully remote position, home office
• Employment Type: Full-time
• Employment Status: Exempt, salaried
• Visa sponsorship is not available for this position.
• Must reside in the United States.
• We are not accepting applicants for remote workers in California, Illinois, and New York at this time.

Compensation:

$98,837 - $154,546, depending on years of experience

Role Overview:

We are building the intelligence layer for industrial operations – transforming raw sensor telemetry, time-series data, and field equipment signals into predictive diagnostics that keep critical assets running.

As a Data Scientist on our team, you will work at the intersection of time-series analytics, machine learning, and engineering domain-knowledge, turning field equipment sensor data, time-series telemetry, and operational data into actionable insights – designing and deploying production-grade solutions for predictive maintenance and anomaly detection across our customers’ industrial environments.

You will partner directly with engineering, product, and domain experts to translate business and operational challenges into scalable, production-ready data science solutions that drive measurable impact on reliability, efficiency, and revenue – with direct visibility into how your work reduces downtime and keeps critical operations running.

We actively support team members to publish, present, and contribute to the industrial AI community.

Key Responsibilities:

• Design, develop, train, and deploy machine learning and AI models that process and analyze field equipment sensor data (time-series IoT, embedded device telemetry) alongside structured and unstructured datasets.
• Build and refine predictive, prescriptive, and anomaly detection models using techniques such as regression, time-series forecasting, classification, clustering, and deep learning to support real-time or near-real-time decision-making.
• Perform exploratory data analysis (EDA), data preprocessing, feature engineering/signal processing, and feature extraction on high-volume, noisy sensor data and multimodal datasets to surface patterns, correlations, and actionable insights.
• Contribute to end-to-end AI workflows, including automated data ingestion, model training pipelines, inference at the edge or in the cloud, and continuous monitoring for model drift and performance degradation.
• Apply statistical modeling, hypothesis testing, and experimentation methods (A/B testing, causal inference where applicable) to validate model performance and ensure robustness in dynamic operational environments.
• Support the development and maintenance of reproducible, scalable ML pipelines using MLOps best practices, including model versioning, retraining, deployment (including edge/embedded constraints), and lifecycle management.
• Collaborate with engineering, product, and domain experts to translate business problems (e.g., predictive maintenance, fault detection, process optimization) into well-defined data science solutions.
• Perform data cleansing, validation, and collation activities to ensure models are accurate, reliable, and aligned with real-world operating conditions.
• Solve complex technical challenges related to analytical toolsets that support engineering and operational decision-making.
• Communicate technical findings, model performance metrics, and business value to internal stakeholders through clear visualizations, written reports, and presentations.
• Explore and evaluate emerging techniques (e.g., generative AI for synthetic sensor data, edge AI optimization, multimodal data fusion) and recommend incorporation into production workflows where appropriate.
• Assist in formulating and managing data-driven project requirements aligned with business needs and strategic company goals.
• Provide subject matter input on analytical tools and methods to cross-functional product development teams.
• Work with software and business development teams to support revenue opportunities tied to data science initiatives and product/service enhancements.
• Support internal resources involved in research, product development, and…