Lead Data Scientist

Epiroc is a leading productivity partner for the mining, infrastructure and natural resources industries. With
cutting-edge technology, Epiroc develops and produces innovative drill rigs, rock excavation and construction
equipment, and provides world-class service and consumables. The company was founded in Stockholm,
Sweden, and has passionate people supporting and collaborating with customers in more than 150 countries.

Lead Data Scientist
Do you like working in an agile and fast paced global environment? Are truly committed, talented and collaborative colleagues something that you are looking for? Are you curious and dare to think new? Welcome to Epiroc and the role as Lead Data Scientist!
Using advanced analytics and machine learning techniques is growing in importance for any company that target increased productivity and growth, but many are struggling to go from proof of concepts to production with enterprise support and monitoring at scale.
At Epiroc we have in close collaboration with our main cloud provider closed this gap and is now exited to onboard a Lead Data Scientist to further improve established practices that enables the company´s AI/ML delivery teams to work autonomous and efficient.

You will be a part of our global IT organization that is responsible for the enterprise scale machine learning platform that enables and empower the whole company to embrace and take advantage of lates AI & ML technology, powered by cloud solutions and agile thinking. 

By joining our Team you can expect an atmosphere of creativity, innovation and workplace diversity. We know that the best teams are built by diversity and inclusion. You will be a part of a skilled group of committed and helpful colleagues with a dare to think new attitude and who all take pride in our core values; Collaboration, Commitment and Innovation.

Your mission 
As advance analytics technics can be utilized throughout Epiroc to improve productivity internally and for our customers and partners there will always be many different teams working in parallel that needs just the right level of alignment, this is where we utilize our enterprise scale machine learning concept as an accelerator.

Your mission as Lead Data Scientist is to be our evangelist for this area, constantly finding ways to further excel and improve our processes, ways of working and to support and train teams that utilize our common platform.

To grow in this position, you will have the possibility to freely try out cutting edge solutions and products, collaborate with leading external expertise, get certification and training in areas needed and more.

Candidates should have three to six years of relevant project experience in successfully launching, planning, and executing data science projects.

Experience using statistics and machine learning to solve complex business problems.

Experience in Azure Machine Learning & Databricks.

Coding knowledge and experience in several languages: for example, R, Python/Jupyter, Scala, etc.

Experience of working across multiple deployment environments including cloud, on-premises and hybrid, multiple operating systems and through containerization techniques such as Docker, Kubernetes, and others.

Ideally, the candidate is adept in agile methodologies and well-versed in applying DevOps/MLOps methods to the construction of ML and data science pipelines.

Certification
Not required but preferably one or more of the following:

AZ-900, Microsoft Azure Fundamentals
AI-900, Microsoft Azure AI Fundamentals
AI-102, Designing and Implementing a Microsoft Azure AI Solution
DP-100, Designing and Implementing a Data Science Solution on Azure
Education
A bachelor’s or master’s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field or equivalent work experience such as, economics, engineering and physics is required. Alternate experience and education in equivalent areas such as economics, engineering or physics, is acceptable. 
Candidates will ideally have a specialization in ML, AI, cognitive science or data science.