Sr. Data Scientist - Industrial Industry Focused

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 ongoing production of data analytics deliverables.

Required Qualifications:

• 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.

Preferred Qualifications:

• Master's or PhD degree in Data Science, Engineering (Electrical, Mechanical, or Chemical), or a related quantitative discipline.
• Background in reliability engineering, condition monitoring, or asset performance management.
• Familiarity with causal inference techniques applied to operational or process data.
• Experience working with multimodal data fusion (e.g., combining sensor data with images, text logs, or maintenance records).
• Experience deploying ML models to edge or embedded devices with compute and memory constraints.
• Familiarity with industrial communication protocols and data sources (e.g., OPC UA, Modbus, MQTT, SCADA, historians such as OSIsoft PI).
• Exposure to digital twin concepts, physics-informed machine learning, or hybrid modeling approaches that combine first-principles engineering models with data-driven methods.
• Experience with generative AI techniques, including the use of synthetic data generation for sensor and operational datasets.

Other Qualifications:

• Successfully pass background check for cybersecurity access requirements.

Cybersecurity Role Expectations:

• Candidate will be responsible for reviewing policies and procedures related to cybersecurity and those relevant to the functions of their role.
• Candidate is expected to maintain a cybersecure work environment.

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
• All other benefits required by applicable law

Alignment with Corporate Values

All Cutsforth employees are expected to perform their work in a manner that exhibits understanding and adherence to the Company Mission and Core Attributes of Cutsforth Employees. Employees in management roles must exhibit continual improvement along Cutsforth’s Leadership Traits. Further, each employee must read and adhere to corporate policies and safety protocols.

• Learn more about Cutsforth here: Cutsforth.com/About
• Read our Mission & Values here: Cutsforth.com/Values

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. Cutsforth will take affirmative action to ensure that applicants are employed, and that employees are treated during employment, without regard to their race, color, religion, sex, sexual orientation, gender identity, or national origin. Such action shall include, but not be limited to the following: Employment, upgrading, demotion, or transfer, recruitment or recruitment advertising; layoff or termination; rates of pay or other forms of compensation; and selection for training, including apprenticeship. Cutsforth agrees to post in conspicuous places, available to employees and applicants for employment, notices to be provided by the provisions of this nondiscrimination clause.

For Cutsforth's full Equal Employment Opportunity Policy, click here: EEO Notice to Employees & Applicants