Senior Solutions Consultant — Industrial AI & Digital Twins

About XMPro and why this role is unusual

XMPro helps industrial organizations improve how they operate by connecting real-time operational data, digital twins, advanced analytics and AI with the people and processes responsible for making operational decisions.

Our technology is used in asset-intensive and process-intensive environments where productivity, quality, reliability, safety and operational performance matter. XMPro brings together operational data, digital twins, machine learning, AI and intelligent decision support to help organizations understand what is happening, determine what is likely to happen next, and take better action.

As our customers mature in their use of digital twins and AI, many are moving toward agentic operations: operational environments where AI agents can continuously interpret context, evaluate conditions, recommend or coordinate actions, and support increasingly autonomous decision-making within appropriate human, engineering and governance controls.

We help customers make that transition pragmatically: starting with trusted operational data and digital twins, building decision-support and predictive capabilities, and progressively introducing governed agentic workflows where they can deliver measurable operational value.

We are looking for an unusual combination of skills: an experienced engineer, preferably with a Chemical Engineering or closely related process engineering background, who has subsequently developed strong data science and AI capabilities. This is not a software development role.

The successful candidate will use XMPro’s low-code/no-code platform to configure, assemble and deliver customer-ready industrial solutions. The focus is on understanding operational problems, defining digital twins, working with industrial data, applying analytics and AI, and configuring practical solutions that customers can use in production.

We want someone who understands real industrial operations because they have worked in them, can work confidently with operational data and modern AI techniques, and enjoys turning complex customer problems into practical solutions.

The role

As a Senior Solutions Consultant, you will work directly with XMPro customers and partners from initial discovery through solution design, build, deployment and adoption.

A major focus of the role will be helping industrial organizations improve manufacturing productivity, process performance and product quality using operational data, digital twins, analytics and AI.

You will work with plant personnel, process engineers, operations teams, data scientists, IT and OT teams, operational leaders and executives to understand how an operation really works, identify the decisions and processes that matter, assess the available data, and design digital solutions around them. You will then configure and deliver those solutions using the XMPro low-code/no-code platform.

The role is predominantly delivery focused, but you will also work alongside our sales team during early-stage customer discussions and occasionally assist with responses to RFPs, RFQs and other technical proposals.

The balance of activities will vary considerably from project to project. We therefore need someone who enjoys variety, learns quickly and is comfortable moving between engineering, data science, solution design, customer engagement and hands-on platform configuration.

What you will do

• Work with customers to understand industrial processes, operating challenges, constraints, objectives and improvement opportunities.
• Translate physical processes, production systems and equipment into meaningful digital twin models, including assets, process stages, relationships, states, events, operating conditions and relevant business context.
• Identify and define high-value industrial use cases, particularly around manufacturing productivity, process optimization and quality improvement.
• Assess operational data sources and determine whether the available data is sufficient to support the intended use case.
• Identify weaknesses, gaps, quality issues, missing instrumentation, inappropriate sampling frequencies, contextualization problems and other data limitations that could materially affect a solution.
• Work with data originating from industrial environments including historians, SCADA, PLC/DCS systems, sensors, laboratory systems, condition monitoring systems, MES, maintenance systems and other OT and enterprise sources.
• Perform exploratory data analysis and develop analytical and AI approaches appropriate to industrial problems.
• Apply techniques including statistical analysis, machine learning, time-series analysis, anomaly detection, predictive modelling, optimization, predictive maintenance, Generative AI and reinforcement learning where appropriate.
• Use engineering knowledge and physical process understanding to challenge purely statistical conclusions and ensure models make sense in the context of the underlying process, chemistry, equipment or operating conditions.
• Build and configure solutions using the XMPro platform, including operational data streams, digital twins, analytics, AI-enabled workflows, recommendations, applications and agentic capabilities.
• Use Python and AI-assisted coding techniques where useful for analysis, modelling, prototyping and integration tasks.
• Work with customers and partners to help them become capable and self-sufficient users of the XMPro platform.
• Conduct workshops, demonstrations, technical discussions and working sessions with customer teams.
• Explain complex engineering, data and AI concepts clearly to audiences ranging from plant engineers and data scientists to operational executives.
• Collaborate with customer IT and architecture teams where solutions involve Azure, AWS, edge, on-premise or air-gapped environments.
• Work with XMPro product, engineering, sales and consulting colleagues to solve customer problems and capture lessons that can be reused across projects.
• Support the sales team where required with technical discovery, demonstrations, solution definition and occasional RFP/RFQ responses.

The background we are looking for

The strongest candidates will have followed a career path that combines real engineering experience with subsequent data science and AI experience.

Our preference is for candidates with a Chemical Engineering background, particularly those who have worked directly with manufacturing, production or continuous and batch process environments before moving into data science, advanced analytics or industrial AI. We are particularly interested in candidates who have worked for approximately 3–5 years or more as a practicing engineer before developing substantial data science and AI capability.

Preferred engineering backgrounds include:

• Chemical Engineering
• Process Engineering
• Mechanical Engineering
• Electrical Engineering
• Controls or Automation Engineering
• Manufacturing Engineering
• Reliability Engineering
• Mechatronics
• Systems Engineering
• Other related engineering disciplines.

What matters most

Candidates from adjacent disciplines are welcome where they can demonstrate strong experience with real operational processes, physical assets and industrial data.

What matters most is that you have actually worked in an industrial environment and understand the realities of production and operations, including process variability, operating constraints, instrumentation limitations, equipment interactions, quality drivers and imperfect data. You should then have developed substantial practical experience applying data science, analytics and AI to real-world problems.

We expect the overall profile of successful candidates to typically represent approximately 7–10+ years of professional experience, although capability and career trajectory are more important than an exact number.

Data science & AI capability

You should be comfortable working hands-on with data and have practical experience with several of the following:

• Python
• Statistical analysis and modelling
• Machine learning
• Time-series analytics
• Anomaly detection
• Predictive modelling
• Predictive maintenance and condition-based approaches
• Process optimization
• Multivariate analysis
• Quality prediction and process performance analysis
• Generative AI and Large Language Models
• Reinforcement learning
• AI-assisted solution development.

Not a software development role

This role does not require software engineering or application development experience.

Python and AI-assisted coding may be used where useful for analysis, modelling, prototyping and integration tasks, but the primary solution-delivery environment is XMPro’s low-code/no-code platform.

We are looking for someone who can combine engineering knowledge, data science and platform configuration to create customer-ready solutions, not someone whose primary career has been software development.

Industrial & OT knowledge

You should understand how data is created and used within industrial environments. Exposure to technologies and systems such as the following is important:

• SCADA
• PLC and DCS systems
• Industrial historians
• OPC UA
• MQTT
• Sensors and instrumentation
• Laboratory and quality systems
• Condition monitoring systems
• MES
• CMMS/EAM and maintenance systems
• Process and production systems
• Industrial networks and OT environments.

Do we have the right data?

You do not need to be an OT controls engineer, but you should understand where industrial data comes from, what it represents, and why that data is frequently incomplete, poorly contextualized, noisy or unsuitable for a particular analytical objective.

You should also understand that meaningful process analysis often requires combining multiple types of information, including process conditions, equipment state, production context, product grade or recipe, quality measurements, laboratory data and operating events, rather than simply analysing individual sensor tags in isolation.

A critical part of this role is being able to look at a desired use case and ask: “Do we actually have the right data to solve this problem, and if not, what is missing?”

Industry experience

Experience in one or more process-intensive, manufacturing or asset-intensive sectors is strongly preferred, including:

• Chemicals and Process Industries
• Advanced Manufacturing
• Food, Beverage and Consumer Products
• Pharmaceuticals and Life Sciences Manufacturing
• Energy
• Oil & Gas
• Mining and Minerals Processing
• Utilities
• Aerospace and Defense
• Transportation and Logistics
• Other complex industrial or operational environments.

Customer & consulting skills

Experience in environments involving process optimization, yield improvement, production quality, throughput, energy efficiency or manufacturing performance will be particularly valuable. Candidates from other sectors will also be considered where they can demonstrate strong engineering, data science and problem-solving capability.

You do not necessarily need to come from a consulting or pre-sales background. You may already be a Solutions Consultant, industrial analytics consultant or solution engineer, or you may be an experienced engineer/data scientist ready to move into a more customer-facing role. Either way, you need to enjoy working with people. You should be able to:

• Ask good questions.
• Understand how a process actually works rather than simply accepting its documented description.
• Communicate effectively with engineers, operators and data scientists.
• Facilitate customer workshops.
• Present technical concepts clearly.
• Challenge assumptions constructively.
• Work through ambiguous problems.
• Develop practical solutions rather than stopping at analysis.
• Learn unfamiliar technologies and processes quickly.
• Work independently while knowing when to draw on specialists.
• Build trusted relationships with customers and partners.

Cloud & architecture

We value curiosity, initiative and an enquiring mind. The right person will naturally want to understand why something happens, not simply identify that it happens.

This is not primarily a Solution Architect position, but experience with broader enterprise architectures will be well regarded. Exposure to any of the following would be valuable:

• Microsoft Azure
• Amazon Web Services
• Edge computing
• Containers
• Hybrid cloud architectures
• On-premise deployments
• Disconnected or air-gapped environments
• Enterprise IT/OT integration
• Industrial cybersecurity considerations.

Defense eligibility

Many XMPro customers operate environments where cloud connectivity is limited or prohibited, so an appreciation of on-premise and air-gapped architectures is particularly useful.

Some XMPro USA customers and projects operate within the U.S. defense environment. Candidates for this position must therefore:

• Be a U.S. citizen.
• Be eligible to obtain and maintain a U.S. Government security clearance.

Travel

A current or previous U.S. Government security clearance is advantageous but is not required.

XMPro performs a significant amount of customer work remotely, but successful solution delivery sometimes requires being where the operation is. You should therefore be comfortable travelling approximately 20–30% as required for customer workshops, implementation activities, demonstrations, site visits and project meetings.

Occasional international travel may also be required. Candidates should therefore hold, or be able to obtain, a valid U.S. passport and be able to travel internationally where required.

Education

A Bachelor’s degree in Chemical Engineering is preferred. Candidates with degrees in Process, Mechanical, Electrical, Manufacturing, Controls, Systems or another relevant engineering discipline will also be considered where they can demonstrate the required industrial and analytical experience.

Postgraduate study in engineering, data science, artificial intelligence, statistics, applied mathematics or a related discipline is advantageous but is not essential where equivalent capability has been demonstrated through experience.

What success looks like

You might have started your career as a chemical engineer working in a manufacturing or process plant, where you developed a practical understanding of how process conditions, equipment, raw materials, operating decisions and production variability interact to affect throughput, yield and quality. You learned that real operations are rarely as clean as the process model, historian or design documentation suggests.

You then became increasingly interested in data. You learned Python, statistics and machine learning and began using them to understand process behavior, identify root causes, predict outcomes and improve operational performance.

Today, you are equally comfortable discussing a process problem with an experienced plant or process engineer, investigating multivariate time-series data in Python, assessing whether the available instrumentation can support an intended use case, designing a digital twin, explaining an AI approach to a data science team, and presenting the resulting solution to a senior customer stakeholder.

You are not restricted by the boundaries of your original engineering discipline. You are curious, technically capable, pragmatic and willing to learn. You understand that the best industrial AI solutions combine engineering knowledge, operational context, trustworthy data and appropriate analytics.

When faced with a new problem, your instinct is: “Let’s understand how this process really works, look at the data, identify what’s missing, and figure out how we can improve it.” If that sounds like you, we would like to talk.