AI/ML Engineer

Does helping to make life better for millions of people by improving healthcare outcomes around the world motivate you? If the answer is yes, you think just like us. We, Mölnlycke, are a world-leading medical solutions company, designing and supplying medical solutions to enhance performance in healthcare – from the hospital to the home.
Continuing to invest in our future, we are looking for a collaborative, creative, and detail-oriented AI/ML Engineer (Data Scientist) who can come on-board and apply deep knowledge of AI/ML to bring Mölnlycke to the forefront of the data-informed decision-making culture.
Our ideal candidate has worked in a data scientist or an AI/ML engineer role wherein he/she is comfortable working with unknowns, evaluating the data and the feasibility of applying appropriate AI/ML techniques to business problems and has a track record of developing and deploying AI/ML models into live applications.
You are comfortable handling big data (both structured and unstructured), with a strong maths, stats, data science and programming background, as well as strong engineering know-how to operationalise AI/ML solutions in the production environment.

Key Accountabilities:
Apply deep technical expertise in solving various real-world business problems, whether it be commercial, operational, or clinical, through the application of AI and Machine Learning
Collaborate with other team members both within and outside the Applied Data Science team to create and deliver world-class data science solutions
Apply advanced machine learning techniques to build prototypes with various AI-related libraries and write production-ready code in Python, according to best coding and object-oriented programming practices
Contribute to developing a business case through a detailed business analysis and process mapping to identify key decision points to address and suggest continuous improvement within the area
Get involved in defining the scope of data science projects and the AI roadmap, working collaboratively with the business stakeholders and other partners, e.g. IT, Legal, Finance, and People
Develop clear, concise communication to explain relevant Machine Learning, data science concepts and model outputs to the business stakeholders
Visualise and translate model outputs into easily understandable, actionable insights for the stakeholders
Research and develop new techniques while helping define an AI ecosystem covering data infrastructure, frameworks, open source tools, data science languages, etc.
Stay up to date with the latest development in Data Science and AI by attending conferences and lead thought leadership by publishing papers


Key Decisions:
Selection of models and technical approach to solve a business problem
Choice of data science tools, frameworks, and data infrastructure to define and build an AI ecosystem, working with IT
AI strategy and roadmap aligned with the business strategies, working with senior leadership and relevant stakeholders
Training requirements and hiring


Capabilities, Experience & Qualifications:
Essential:
PhD or master’s degree in a STEM field involving machine learning, computer science, and statistics
5-10 years' professional experience in data science
A track record of successfully building and productising AI solutions using various ML techniques
Research and innovation skills with the ability to understand and explain to others, publications on advanced machine learning and computer science as well as the ability to prototype, evaluate and adapt / improve the ideas discussed in the publications
Hands-on experience in frameworks like SciKit-Learn and TensorFlow / PyTorch
Good grasp of classical statistical methods, e.g. fitting regression models, inference testing and sampling
Excellent programming skills in Python (Object-Oriented Programming), with a good understanding of coding practices and version control software such as Git
Practical cross-functional experience in R&D, marketing, sales, finance, and operations (ideally in the life science industry)
Data wrangling with tools like Hive and Spark and libraries like Pandas, NumPy and SciPy
Hands-on experience in using various techniques to handle data issues, e.g. a lack of data, missing data, imbalanced data, incorrect data, outliers, etc.
Experience with Rest APIs and CLIs
Excellent communication and teamwork
High proficiency in English


Desirable:
Experience in any of the following:
NLP / NLU / NLI – topic modelling, word embeddings, semantic ontology, etc.
OCR – document processing and data extraction
Computer vision and image processing
Experience in setting up an AI ecosystem to enable building, training and deployment of AI/ML models
Experience in ML Life Cycle Management, i.e. containerization (Docker, Kubernetes), APIfication, MLOps, etc.
Familiar with client-server and microservice architecture
Experience of working in cloud environments, e.g. Azure, Snowflake
MedTech / Life Science domain experience
Agile development skills and experience