Sr. Data Scientist - Industrial Industry Focused

Role Information

• Work Location: Fully remote position, home office
• Employment Type: Full-time
• Employment Status: Exempt, salaried
• Visa sponsorship: Not available
• Residency: Must reside in the United States
• Exclusions: 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.

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.
• Perform exploratory data analysis (EDA), data preprocessing, feature engineering/signal processing, and feature extraction on high-volume, noisy sensor data.
• 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.
• Apply statistical modeling, hypothesis testing, and experimentation methods to validate model performance.
• Support the development and maintenance of reproducible, scalable ML pipelines using MLOps best practices.
• Collaborate with cross-functional teams to translate business problems into well-defined data science solutions.
• Communicate technical findings, model performance metrics, and business value to internal stakeholders.
• Explore and evaluate emerging techniques (e.g., generative AI, edge AI optimization) and recommend incorporation into production workflows.

Required Qualifications

• Bachelor's degree in Engineering (Mechanical, Electrical, Chemical, or Aerospace strongly preferred) with formal training in data science/ML.
• 5 years of professional experience in data science, machine learning, signal processing, and applied analytics (Master's or PhD may substitute for up to 2 years).
• Direct industry experience in Power Generation, Oil & Gas, Aerospace, Pulp & Paper, Manufacturing, or similar.
• Demonstrated experience working with time-series, sensor, and operational/IoT data.
• Independently owned at least one ML model from prototype through production.
• Experience supporting predictive maintenance, fault/anomaly detection, or asset health monitoring.
• Proficiency in Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch), SQL, time-series databases, and visualization tools.
• Hands-on experience with time-series modeling (e.g., ARIMA, Prophet, LSTMs, transformers).
• Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices (MLflow, Airflow, Docker, CI/CD).
• Excellent communication skills and ability to collaborate across teams.

Preferred Qualifications

• Master's or PhD degree in Data Science, Engineering, or a related quantitative discipline.
• Background in reliability engineering, condition monitoring, or asset performance management.
• Familiarity with causal inference, multimodal data fusion, or edge/embedded deployment.
• Experience with industrial communication protocols (OPC UA, Modbus, MQTT, SCADA, OSIsoft PI).
• Exposure to digital twin concepts, physics-informed machine learning, or generative AI.

Cybersecurity Role Expectations

• Candidate will be responsible for reviewing policies and procedures related to cybersecurity.
• Candidate is expected to maintain a cybersecure work environment.
• Successfully pass background check for cybersecurity access requirements.

Benefits

• Paid Time Off
• Medical, Vision, Dental Insurance
• Health Savings Account with Employer contributions
• 401(k) with Employer match
• Short-term & Long-term Disability Coverage
• Accidental Death & Dismemberment Coverage
• Life Insurance Coverage
• Eight paid holidays per year

Equal Employment Opportunity Statement

Cutsforth will not discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, or national origin.