Data Science Engineer (India)

QiO Introduction

QiO is an Industry 4.0 technology company built by industrial engineers to harness data for better business performance and accelerate progress towards a zero carboneconomy.

Since 2015, our Foresight Sustainability Suite of products have helped industrial clients make step changes in asset efficiency, maintenance and reliability, resulting in carbon footprint reduction.

Recognised in Gartner’s lloT Magic Quadrant report, the 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 at speed.

You

QiO Technologies is looking for a Data Science Engineer who is focused and passionate about building large-scale systems that take the best of open-source technologies but provide world-class security. You understand that scale is not thousands of active points but tens of millions.

Being an integral part of a product development team is important to you. You realise the benefit and significance of investing time in sharing knowledge within the team, knowing that teamwork is the driver to your success at QiO.

You want to work with some of the most talented people from around the world in a company focused on liberating engineers and unleashing their talent.

Location

This role is specific to Pune, Maharashtra, India. Therefore we would ideally like to see candidates from Pune and commutable areas.

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

Roles & Responsibilities

Manage customers throughout pre-sales, onboarding, blueprint, implementation, and project completion.
Able to create Point of View based on the customer requirement and Product fit.
Able to position, build requirement documents, create Point of view by interacting with customers focusing on QiO Foresight Sustainability Suite of software solutions.
Architect and define customer success with customer-specific requirements.
Able to analyse large data sets and look for optimisation opportunities and application of AI\ML and predictive capabilities.

Knowledge

We want candidates who love data science and are excited about what they do.

If you excel in any of these areas, let us know!

Deep understanding of the Manufacturing sector (discrete\ process) and IT\OT applications landscape.
Good understanding of the application of AI\ML\Analytics to optimise production, energy carbon, cost, quality, asset efficiency and performance.
Familiar with concepts and technologies around Software as a Service (SaaS), big data, cloud, Industry 4.0, OT, IoT, IIoT and MES.
Proficient in MS Office products (Excel, Word, MS Outlook, MS PowerPoint).

Behavioural Skills

Excellent cross-function communication and collaboration.
Ability to build relationships with internal and external teams.
Entrepreneurial, high energy, tenacious and resilient.
Able to work in high energy, dynamic “startup” environment.
Exceptional English communication skills (written and oral).
Strong presentation skills with the ability to articulate the proposition for easier comprehension by customers, partners, and internal team members.

Qualifications

5 – 10 years work experience
Bachelor’s Degree in an Engineering discipline is a must
MBA Degree (ideal but not a pre-requisite)
AI\ML\Analytics Certification

To Apply

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

COVID & Hiring

As we navigate through the pandemic, we want to reassure you that keeping candidates and our employees safe is of paramount importance to us. That’s why the entire recruitment process will take place remotely, using phone, video calls and face to face meetings as appropriate.

For successful candidates, we always ensure a safe, engaging and (for the time being) remote onboarding experience.

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.