Cutsforth Inc.

Data Scientist

• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
• Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
• Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Data Scientist – Predictive Maintenance

Engineer

• Applies data science and machine learning to the analysis of radio frequency and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions across industrial, communications, and defense-adjacent applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on RF, acoustic, and time-series sensor data, including beamforming, BSS, spectral subtraction, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw demodulated RF and acoustic data to surface patterns and anomalies.
• Analyze and interpret signals from various electrical asset monitoring systems utilizing RF, acoustic, and signal processing expertise to support fault isolation and anomaly detection.
• Use asset monitoring sensor data as measurement to characterize and validate signal data.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and LRU level — identifying root causes from spectral, RF, and acoustic sensor data in complex industrial systems.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Signal Processing Engineer – RF, Acoustics

Recruitment

• Supports Cutsforth’s hiring efforts by sourcing, screening, and coordinating candidates across open roles
• Partners with hiring managers and the HR Team to keep the recruiting pipeline moving and to deliver a positive candidate experience from first contact through offer
• Source candidates through job boards, LinkedIn, referrals, and other channels
• Review applications and resumes to screen candidates against role requirements
• Conduct initial phone screens and document candidate qualifications
• Schedule and coordinate interviews between candidates and hiring managers
• Maintain accurate candidate and requisition data in the applicant tracking system
• Draft and post job openings to the careers page and external job board
• Communicate with candidates throughout the process and respond to inquiries promptly
• Coordinate reference checks and pre-employment requirements
• Support offer preparation and new-hire handoff to onboarding
• Track recruiting metrics and provide status updates to the CSOO and hiring managers

Recruiter

Cloud Engineer

• Designs, governs, and optimizes Cutsforth’s enterprise Azure cloud platform, establishing the landing zones, network topology, identity model, and security controls that host the company’s software and data products
• Partners with software engineering, security, and operations to deliver scalable, secure, and cost-effective cloud architecture that supports Cutsforth’s connected monitoring and industrial product offerings

Senior Azure Cloud Architect

Data Scientist – RF/Acoustics Signal Processing

Account Manager

• Develop positive relationships in multiple areas of the customer organization.
• Uncover customer needs in line with Cutsforth solutions by executing sales tactics and account planning.
• Maintain contact with clients, answer questions, resolve conflicts, and provide solutions to issues that arise.
• Coordinate Cutsforth resources to ensure customer success and sustain revenue growth.
• Stay up to date on industry and market trends impacting the target accounts.
• Sustain multiple quarters of sales pipeline at all times, with the ability to provide a forecast with certainty to leadership.
• Develop relationships and coordinate with partner companies to meet customers’ needs.
• Attend trade shows and conferences as needed.
• Provide feedback to the organization on products, services, and internal processes based on experience with customers.
• Keep the CRM pipeline and account information up to date in line with company goals and expectations.

Account Manager – Industrial New Markets

Full-stack Engineer

• Design, develop, test, and deploy scalable web applications and APIs across the full stack — from database schema to UI components
• Create AI applications where appropriate to enhance the business
• Lead architectural decisions and code reviews, ensuring high standards for maintainability, security, and performance
• Participate actively in R&D efforts: evaluate new technologies, build proof-of-concept prototypes, and contribute findings to engineering roadmap discussions
• Collaborate with product and design teams during sprint planning and discovery sessions to shape technical solutions
• Optimize application performance, identify bottlenecks, and implement improvements across front-end and back-end systems
• Write comprehensive unit, integration, and end-to-end tests to ensure software reliability
• Contribute to internal technical documentation, architecture diagrams, and R&D reports
• Mentor junior developers through pair programming, code reviews, and knowledge-sharing sessions
• Stay current with industry trends and proactively recommend tooling or process improvements
• Participate in on-call rotation and support incident response as needed

Full Stack Developer

Administration

• Own end-to-end Business Central system configuration across all active modules: Finance, Supply Chain, Manufacturing, Service Management, and Project Accounting
• Manage user accounts, permission sets, and security role assignments; maintain least-privilege access discipline; conduct quarterly access audits
• Administer company setup, posting groups, dimension codes, number series, fiscal period configuration, and multi-currency settings for 23+ country operations
• Manage the BC administration center: environment management, sandbox provisioning, telemetry monitoring, and support ticket escalation
• Own the bi-annual Microsoft release wave cycle — evaluate release notes, configure and test in sandbox, coordinate UAT with module owners, and deploy to production with documented change control
• Design, build, and maintain BC native approval workflows across purchase orders, sales orders, journal entries, production orders, and service management
• Build and maintain Power Automate cloud flows connecting Business Central to Salesforce (CRM/CPQ/Field Service), Microsoft 365, and other business applications
• Translate business process requirements from Finance, Supply Chain, and Operations into automated workflow rules with appropriate approval routing, exception handling, and audit trails
• Govern the Power Platform environment: manage DLP policies, connector permissions, flow ownership, and environment strategy
• Design and maintain automated alerts, notifications, and scheduled jobs (job queue entries) for period-end processes, inventory thresholds, and compliance triggers
• Own the iPaaS integration layer (Celigo or Rapidi) connecting Salesforce CRM/CPQ/Field Service to Business Central — monitor sync health, manage error queues, resolve failures, and coordinate with the Salesforce admin on cross-system issues
• Administer the tax engine integration (Avalara or Vertex): manage nexus configuration, product taxability codes, exemption certificates, and coordinate with Finance for audit readiness
• Manage ISV application administration: revenue recognition module (ASC 606 deferral schedule configuration), subscription/recurring billing engine, and any additional certified AppSource extensions
• Maintain API credentials, OAuth connections, and service accounts for all BC integrations; enforce rotation schedules and access documentation
• Serve as primary technical contact with implementation partner, ISV vendors, and Microsoft support

ERP Administrator – Microsoft 365 Dynamics

Product Manager

• Own the product backlog for our AI-assisted diagnostics platform.
• Translate strategic direction from the Senior Product Manager into prioritized, executable work.
• Drive product execution, leading agile ceremonies alongside engineering.
• Ensure scope clarity, unblock decisions, and keep the engineering team moving against committed work.
• Partner with the product designer to shape user experiences, providing product direction and review on design output, and helping ensure workflows are intuitive for industrial operators and analysts.
• Work closely with engineering to help ensure clarity on scope, acceptance criteria, and user outcomes.
• Own quality through delivery.
• In partnership with engineering leads, define and uphold the bar for ’done.’
• Review work as it moves through delivery to ensure it meets acceptance criteria and user outcomes.
• Synthesize customer and SME feedback into the roadmap, maintaining feedback loops from internal services teams, customer-facing engagements, and SME input.
• Surface prioritization recommendations to the Software Product Lead.
• Define and track product success metrics such as workflow efficiency, user adoption, diagnostic accuracy, and customer satisfaction.
• Communicate clearly to internal stakeholders, providing consistent updates on product progress, risks, and decisions across engineering, services, sales, and leadership.
• Lead discovery for AI/ML-powered features through research, SME engagement, and documented findings that shape requirements.
• Partner with sales, customer success, and marketing teams on product knowledge, documentation, and launch enablement.
• Proactively learn industry trends in industrial data, UX, and AI applications, bringing relevant insights to the team.
• Coordinate with cross-functional teams to support on-time releases and alignment with business priorities.

Associate Product Manager

Marketing

• Assist with event planning and logistics, such as packing and shipping materials for tradeshows, along with creating, promoting, and executing webinars
• Assist with social media marketing, mainly targeting LinkedIn
• Create and audit content pieces, both customer-facing and internal, and submit them through the required review process prior to publication
• Commit to continual self-guided education to gain relevant knowledge on product and service offerings, tech stack, relevant industries, clients, talk tracks, and general marketing practices
• Meet weekly with supervisor to review activities and ensure alignment with overall company priorities
• Track relevant metrics for marketing activities such as overall impressions, conversions, and company revenue impact
• Consistently demonstrate a positive and professional image of Cutsforth with exceptional written and verbal communication
• Ability to learn current Cutsforth marketing software programs (Salesforce, ZoomInfo, Gong, Constant Contact, etc.)

Marketing Specialist Intern

• 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.

Senior Data Scientist – Industrial Focus

• Designs, integrates and implements software of embedded devices and systems.
• Analyzes, studies and improves the efficiency and stability of the system.
• Executes a complete embedded software development lifecycle.
• Develops, codes, tests and troubleshoots the system software.
• Supports company products with embedded software tools for data collection from sensors.
• Collaborates with customers, internal teams, and vendors to fix and improve products.
• Maintains and implements cybersecurity protocols as required.
• Participates in code reviews to analyze and improve code.