Roche is looking for an Expert Data Scientist to build and deploy ML models and develop AI agents for tasks involving unstructured/structured data and workflow automation to prevent, stop, and cure diseases and ensure access to healthcare.
Requirements
- machine learning (ML)
- data science
- software engineering
- Python
- Scikit-learn
- Pandas
- TensorFlow
- PyTorch
- MLOps
- AWS
- Azure
- GCP
- CNNs
- RNNs
- NLP
- BERT
- GPT
- Hugging Face ecosystem
Responsibilities
- Machine Learning and Deep Learning: The candidate must be proficient in a wide range of ML algorithms, from traditional models like linear regression and decision trees to more advanced deep learning architectures such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). They should understand the principles behind model training, validation, and hyperparameter tuning
- Natural Language Processing (NLP): For extracting information from unstructured text, strong NLP skills are essential. Look for experience with techniques like tokenization, sentiment analysis, named entity recognition, topic modeling, and using pre-trained language models like BERT, GPT, or others from the Hugging Face ecosystem
- Data Handling and Feature Engineering: They should be adept at working with various data formats and have experience in data cleaning, preprocessing, and transforming raw data into useful features for ML models. This includes handling missing values, encoding categorical data, and scaling numerical features
- Programming and MLOps: Proficiency in Python is a must, along with a solid understanding of key libraries like Scikit-learn, Pandas, TensorFlow, and PyTorch. Experience with MLOps (Machine Learning Operations) practices, including model versioning, monitoring, and deployment on cloud platforms (AWS, Azure, or GCP), is crucial for building and maintaining robust solutions
- AI Agent Architectures: Look for a candidate who understands the components of an AI agent, including a Large Language Model (LLM) as the brain, tools for specific tasks, and a logical structure for decision-making
- Workflow Automation: The candidate should have practical experience in designing and implementing automated workflows. This involves integrating AI agents and ML models into existing business processes. They should be able to identify bottlenecks, map out a solution, and build the necessary connectors or APIs to execute tasks automatically
- Unstructured Data: The candidate needs to demonstrate expertise in handling various forms of unstructured data, including text, images, and audio. This involves building pipelines to ingest, process, and analyze this data to extract meaningful insights or trigger actions
Other
- Problem-Solving: The ability to break down complex business problems into manageable, data-driven solutions is key. They should be able to think critically and creatively to solve real-world challenges
- Communication: A great candidate can clearly articulate technical concepts to non-technical stakeholders, explaining the "why" and "how" of their solutions. This is vital for collaborating with different teams and ensuring the project meets business goals
- Business Acumen: The best candidates understand the business context of their work. They should be able to connect their technical solutions directly to a positive impact on the company's bottom line or operational efficiency
- Relocation benefits are not available for this posting
