Senior Data Scientist – Industrial Focus

Cutsforth Inc.

Data Scientist transforming raw sensor telemetry into predictive diagnostics that ensures asset reliability in industrial operations. Collaborating across engineering and product teams for data-driven solutions.

Posted 5/23/2026full-timeRemote • California, Illinois, New York • 🇺🇸 United StatesSenior💰 $98,837 - $154,546 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSAzureCloudDockerGoogle Cloud PlatformIoTNumpyPandasPythonPyTorchScikit-LearnSQLTableauTensorflow

About the role

Key responsibilities & impact

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 ongoing production of data analytics deliverables.

Requirements

What you’ll need

Bachelor's degree in Engineering required; Mechanical, Electrical, Chemical, or Aerospace strongly preferred.
Formal training or demonstrated proficiency in data science, machine learning, and applied analytics required.
5+ years of professional experience in data science, machine learning, signal processing, and applied analytics; Master’s or PhD in a relevant field may substitute for up to 2 years of required experience.
Direct industry experience required in one or more of the following sectors: Power Generation, Oil & Gas, Aerospace, Pulp & Paper, Manufacturing, or similar industries.
Demonstrated experience working with time-series data, sensor data, and operational/IoT data within an industrial environment.
Has independently owned at least one ML model from prototype through production, including monitoring and retraining in a live environment.
Experience supporting use cases such as predictive maintenance, fault/anomaly detection, asset health monitoring, or process optimization.
Proficiency in Python (NumPy, pandas, scikit-learn, TensorFlow/PyTorch), SQL, time-series databases (InfluxDB, TimescaleDB, Snowflake), and visualization tools (Power BI, Tableau, Plotly).
Hands-on experience with time-series modeling techniques (e.g., ARIMA, Prophet, LSTMs, transformers for sequence data).
Practical experience with anomaly detection methods on streaming or batch sensor data.
Familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices including MLflow, Airflow, Docker, and CI/CD pipelines.
Strong analytical and problem-solving skills with attention to detail.
Excellent written and verbal communication skills, with the ability to present complex findings to non-technical audiences.
Effective collaborator across engineering, product, and business teams.
Self-motivated and capable of managing multiple priorities in a fast-paced environment.
Active contributes to the broader data science and industrial AI community through open-source projects, technical publications, conference presentations, or patents; a track record of knowledge sharing is valued and supported.

Benefits

Comp & perks

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

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata sciencesignal processingpredictive maintenanceanomaly detectiontime-series modelingfeature engineeringstatistical modelingdata preprocessingexploratory data analysis

Soft Skills

analytical skillsproblem-solvingcommunication skillscollaborationself-motivationattention to detailproject managementknowledge sharingpresentation skillsadaptability

Certifications

Bachelor's degree in EngineeringMaster’s degree in relevant fieldPhD in relevant field