About this AI Consultant Intern role at Aizon
We are a fast-growing company entering an exciting new stage of expansion, and we are looking for ambitious new team members who are motivated to directly impact our growth and success. If you want your work to make a visible difference as we scale, this is the place to do it.
Aizon is helping pharma manufacturers and CDMOs make better decisions in GMP operations.
- What makes us different from other manufacturing platforms? We help operations leaders and engineers avoid endless cycles of chasing data in disparate and heavy-handed systems and rapidly understand how to run their manufacturing processes better.
- We’re solving multiple problems in GMP operations that cost manufacturers millions yearly while accelerating their digital maturity journey. One of our unique capabilities is the ability to operationalize the use of predictive AI models in real time without a big data science staff.
- We're backed by leading industry and software investor firms with solid industry and technology expertise, giving us the foundation to grow with confidence.
Join us if you are motivated to directly impact our company's success and growth path forward and, more importantly, to positively contribute to the life science industry and deserving patients worldwide.
The Position
Reporting to an ACS AI Team Lead, the Artificial Intelligence Intern will inspire our customers through technology, using their growing knowledge of Artificial Intelligence, Cloud Computing, and Software as a Service.
Responsibilities:
Collaborate with AI and Data Consulting teams to analyze complex data, solve client business problems, and deploy scalable solutions into production.
Being a great business influence, linearly linked to the Aizon platform.
Generalize learnings from our AI solutions to help develop new AI-based Native Apps of the Product.
Assist in the training and fine-tuning of Machine Learning models for the Manufacturing Industry, including supervised/unsupervised models, probabilistic models, linear models, text classification, computer vision, and time-series models.
Identify bottlenecks in the ETL pipelines and recommend robust software solutions at scale.
Contribute to a SaaS life cycle through initial prototyping to enterprise-quality testing and final implementations by using Agile methodologies.
Help to validate algorithm implementations in order to meet the rigorous standards of quality set for the Pharmaceutical Industry.
Analyze our customers' data in order to model the behavior of a wide variety of pharma manufacturing processes.
Build AI-driven solutions that fit the needs of the Company's customers, using the tools, algorithms, and software provided by the product developed by our Company.
Help us to build intelligent and reusable solutions for our customers and the product.
Knowledge and Experience:
Must - have
Educational background (completed or in progress) in Computer Science, Mathematics, Statistics, Advanced Analytics, or another similar quantitative field - Data Science and Machine Learning are highly beneficial.
Intermediate experience with Python and a general understanding of programming principles and techniques.
Comfortable with Machine Learning-related libraries such as pandas, scikit-learn, TensorFlow, and Keras.
Proficient in spoken and written English.
Understanding of JavaScript React.
Experience working with both relational and NoSQL databases.
Sound knowledge of various operating systems and databases.
Experience in data visualization and dashboard creation tools.
Knowledge of Biotechnological Processes (e.g., fermentation, chromatography, viral inactivation) and Regulations.
Skills:
Ability to understand business requirements and translate them into technical requirements.
Capable of working with a dynamic product that is ever-changing and evolving rapidly.
Strong analytical and creative problem-solving skills.
Ability to take smart risks and champion new ideas.
Team player, collaborative, innovative, get things done mind.
Show initiative in setting and meeting goals within an environment of managed change.
Ability to work under pressure, responsible for multiple important tasks in parallel.
Commitment to quality.
