Cutsforth Inc.

• 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

• 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

• Regularly design, develop, and maintain data pipelines that support operational, analytical, and machine learning workloads • Write clean, efficient, and well-documented Python code for data processing and automation tasks • Support the integration and monitoring of AI/ML models in production by delivering reliable, well-structured data pipelines • Communicate technical findings and recommendations clearly to both technical and non-technical stakeholders across teams • Participate in cross-functional meetings and planning sessions to align data engineering efforts with broader business goals • Stay current with advancements in data engineering tools, techniques, and industry-specific applications, particularly within power, oil, and gas environments • Document processes, data models, and systems in a way that supports knowledge sharing and team continuity

• 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.

• 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.

• 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

• 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.

• 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.)

• 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.

• Regularly design, develop, and maintain data pipelines and ML workflows to support operational and analytical needs • Write clean, efficient, and well-documented Python code for data processing, model development, and automation tasks • Analyze and interpret complex datasets, including time-series and machine health data to surface meaningful insights and support decision-making • Build, validate, and monitor AI/ML models in production, proactively identifying and resolving performance issues • Communicate technical findings and recommendations clearly to both technical and non-technical stakeholders across teams • Participate in cross-functional meetings and planning sessions to align data engineering efforts with broader business goals • Stay current with advancements in AI/ML techniques, tools, and industry-specific applications, particularly within power, oil, and gas environments • Document processes, models, and systems in a way that supports knowledge sharing and team continuity