Senior Global Product Manager (UK or India)

QiO Introduction

QiO Technologies is an industrial IoT SaaS company delivering digital transformation in energy intensive and asset heavy industries.

The company was established in 2015 by industrial engineers and has invested over 100+ person years to create a world leading suite of AI driven IoT suite of enterprise applications.

Recognised in Gartner’s lloT Magic Quadrant report, QiO’s Foresight Sustainability Suite combines our proprietary out-of-the-box industry-specific blueprints and automated machine learning algorithms for rapid deployment to multiple plants and asset types.

The QiO Foresight Sustainability Suite delivers measurable difference in operational efficiency and business performance by enabling a step change in asset efficiency, maintenance and reliability.

Role & Responsibilities

The ideal candidate will have experience leading and executing a multi-year product strategy, road map and business plan for our Foresight suite of products.
You will own, identify, prioritise, develop and validate QiO’s existing and new solutions in IIoT/Industry 4.0 and digital transformation of the manufacturing sector.
Be an evangelist who can create and deliver the product vision across pitch decks, website copy, white papers, demos, blogs, and social media.
You will undertake customer, market and competitive research to enhance our products.
You will work closely with the head of product, CTO, sales and marketing teams to manage the entire product development life cycle.
Define product requirements and manage and monitor development of product features.
Identify, prioritise, and drive product development with the engineering team.
Work closely with customers, partners, and prospects to generate new ideas, and validate new features and products.
Develop positioning and messaging to differentiate our products in the market.
Deliver go-to-market strategies, pricing, and customer references.
Foster innovation, insights, and engagement across the organisation to identify future product opportunities to create growth and value.
Develop collateral and sales tools that support customer acquisition.
Develop and deliver product demos and training for sales support.
Create referenceable customers through the full lifecycle of pre-sales, implementation, and beyond.
Support sales and senior leadership to track and support all aspects of growth, including pipeline, sales targets, new customer acquisition, and customer satisfaction.
Build and maintain a product management organisation over a period of time.

Location

The location for this role is flexible – UK or India.

Unfortunately, we are not able to support relocations or visa sponsorship.

Knowledge

10+ years of international experience driving product management for leading B2B software companies.
Hunter mindset.
Full lifecycle product management experience.
Experienced product marketing leader, who has helped scale a fast-growing SaaS business focused on Industry4.0.
Global perspective of product management, client management, and ability to travel.
Stakeholder and customer relationship management experience.
Ability to clearly and concisely present product value and benefit, proposals, and status to peers and management.
Ability to clearly define requirements and prioritise requests based on qualitative and quantitative analysis.
Operational excellence, ‘win and results’ driven.
Entrepreneurial, high energy, tenacious and resilient approach.
Commercially astute.
Management and Leadership experience.
Can operate in ambiguity.

Skills & Experience

Sales Life cycle, Marketing, Product Management knowledge.
Software as a Service (SaaS).
Deep understanding of the changing digital manufacturing landscape of Industry 4.0/IIoT and the resulting opportunities.
Experience of developing Product Requirements and Market Requirements Documents.
Deep understanding of the changing landscape of Asset Efficiency, Asset Maintenance and Asset Service for the manufacturing sector.
Ability to fluidly navigate between business and technical concepts.
Awareness of competitors, their products and market positioning.
Proficient in product demonstrations and creating product collaterals.
Familiar with concepts and technologies around big data analytics, Industrial IoT, sustainability, asset health and asset service in manufacturing and utility verticals.
Strong analytical skills
Proficient in Atlassian suite (Jira, Confluence) and product management tools (Aha/Productboard)
Proficient in MS Office products (Excel, Word, MS Outlook, MS PowerPoint)

Qualifications

MBA or Bachelor’s Degree in Business or Engineering discipline, with a marketing focus

To Apply

To apply for this role, please email your application including a cover letter and CV to recruitment@qio.io

The recruitment process will take place remotely, using phone, video calls and face to face meetings as appropriate.

Equal Opportunity

We know that diverse teams are strong teams and welcome those with alternative identities, backgrounds, and experiences to apply to join our team.

DEUTSCHE

Die vierte industrielle Revolution, oder Industrie 4.0, ist gekennzeichnet durch das intelligente Vernetzen von Maschinen, Produkten und Mitarbeitern mit cyber-physikalischen Systemen in intelligenten Fabriken. Die Produktion in intelligenten Fabriken wird hochflexibel. Die Optimierung der Wertschöpfungskette erfolgt darin basierend auf Echtzeit-Anforderungen, Mitarbeiter werden von intelligenten Kontrollsystemen profitieren. In der intelligenten Fabrik wird die Kombination aus Material, Lieferanten, Ausrüstung und qualifizierten Mitarbeitern so organisiert, dass sie Einheiten produzieren, die den Kundenanforderungen in Echtzeit genügen. Die Personalisierung auf Kunden- und Marktsegmente wird durch integrierten Bedarfssignale gewährleistet, um skalierbare Geschäfte zu erzielen. Die intelligente Fabrik erfordert eine flexible und agile vernetzte Produktion.

Flexibilität bedeutet, dass die Betriebsmittel in der Lage sein müssen, eine Vielzahl unterschiedlicher Produktionsprozesse durchzuführen. Die Mitarbeiter müssen über ein sehr breites Spektrum an Qualifikationen und Erfahrungen im Umgang mit verschiedenen Systemen und Methoden verfügen.

Darüber hinaus erfordert die Flexibilität eine sehr hohe Verfügbarkeit der Einsatzmittel. Vernetzt bedeutet, dass alle relevanten Informationen für die Herstellung eines Produkts in Echtzeit zwischen allen Systemen und den am Prozess beteiligten Personen ausgetauscht werden. Tritt beispielsweise bei kritischen Anlagen ein Fehler auf, müssen neue Wege durch die Produktion gefunden werden, um Aufträge termingerecht zu erfüllen. Um das zu erzielen müssen alternative Arbeitspläne und die daraus resultierenden Produktionskosten und -zeiten analysiert werden. Neue Produktionseinheiten entstehen durch die neue Kombination von Ressourcen, Ausrüstung und Personal pro Auftrag. Dies erfordert eine enge Interaktion zwischen Ausrüstung, Ressourcen und Mitarbeitern mit einem intelligenten Produktionssystem, das sowohl die alternativen Routen als auch die Produktionskapazitäten verwaltet. Daher ist die Integration von Daten aus verschiedenen Systemen intern und extern unerlässlich.

Ziel dieses Forschungsprojekts ist es, Techniken zur Realisierung schlanker intelligenter Fabriken zu entwickeln, umzusetzen und zu validieren:

Das erste Projektziel ist die Entwicklung von neuen Algorithmen die den aktuellen Zustand der Fabrik bei der Erstellung des Produktionsplans berücksichtigen.

Das zweite Projektziel ist die Entwicklung einer Methodik für das Lösen von Systemen mit multiplen und teilweise probabilistischen Randbedingungen basierend auf reinforcement learning techniques.

Das dritte Projektziel ist es, eine mathematische Darstellung des genauen Zustands und Verhaltens der Produktionsanlagen zu generieren, die QiO den PARCS score nennt.

In diesem Arbeitspaket werden hochdimensionale teilweise diskrete Optimierungsprobleme mit einer Vielzahl von Nebenbedingungen mit modernen mathematischen Verfahren bearbeitet.

ENGLISH

The fourth industrial revolution, or Industry 4.0, is marked by the intelligent networking of machines, products and employees with cyber-physical systems in Smart Factories. Production in Smart Factories will be highly flexible, highly productive and resource efficient. The optimization of the value chain is based on real-time requirements. At the Smart Factory, the combination of materials, suppliers, equipment and skilled employees is organized to produce units that meet customer demands in real time. Personalization to customer and market segments is ensured by integrated demand signals to achieve scalable business.

The Smart Factory requires flexible and agile networked production. Flexibility means that the operating resources must be able to carry out a variety of different production processes. Employees are required to have a very broad range of qualifications and experience in using different systems and methods. Furthermore, flexibility requires a very high availability of operating resources.

Networked means that all the relevant information for the manufacture of a product is shared in real-time between all of the systems and people involved in the process. This requires information about potential machine breakdown, delays to a supplier order, missing parts, personnel availability or available skills.

If a fault occurs to critical piece of equipment, for instance, new routes through production must be found in order to fulfil orders on time. In order to do this, alternative work schedules and the resulting production costs and times must be analysed. New production units arise through the new combination of resources, equipment and personnel per order. This requires close interaction between equipment, resources and the workforce with a master intelligent production system, which manages the alternative routes as well as production capacity.

As such integration of data from diverse systems internal and external is therefore essential. The aim of this research project is to develop, implement and validate techniques for the realization of Smart Factories:

The first project objective is the development of new algorithms that take into account the current state of the factory when creating the production plan.

The second project objective is the development of a methodology for solving systems with multiple and partially probabilistic constraints based on reinforcement learning techniques.

The third goal of the project is to generate a mathematical representation of the exact condition and behaviour of the production facilities, which QiO calls the PARCS™ score. It is designed to enable engineers within software tools such as the QiO Foresight Engine™.

This work package deals with high-dimensional, partially discrete optimization problems with a variety of constraints using modern mathematical methods. The results of the project proposed here go well beyond the general state of the art, especially in the fields:

Artificial intelligence for the dynamic learning and modeling of production processes

Artificial intelligence for production planning and supply chain management

Traditional approaches to modeling and describing production processes assume a well-known problem and seek a solution to a specific problem. This approach is not suitable for solving such problems in the context of an intelligent factory. The boundary conditions that describe the processes change regularly here and they are mostly of a probabilistic nature. We plan to learn the processes dynamically based on the data produced by a production hall. We call such dat, the voice of the production hall.

Such automated learning is important because constructing a graphical model by a human expert can be cumbersome and time-consuming. If, as is often the case, there is a database of examples, learning techniques can do at least part of the work. This approach should lead to an always current and dynamic view on the production processes. It should also form a mathematical basis for dynamic production planning as it will be described in the next.