Data Scientist (Ai Ml) Bardi

Data Scientist (Ai Ml)
Bardi
Emilia Romagna, Bardi

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 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 masters 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