Senior Applied Scientist

Description:

We are looking for multiple Senior Applied Scientists, they are responsible for building AI and machine learning models and pipelines, with a particular emphasis on generative AI, large language models (LLMs), and predictive modeling. This role involves collaborating closely with business and product stakeholders to understand data requirements and product specifications, enabling data-driven decision-making and product design. Additionally, the position requires coordination with software engineers and developers to deliver effective solutions.

A key aspect of the role involves rapidly acquiring new tools and technologies, leveraging a strong foundation in machine learning and artificial intelligence alongside basic programming and scripting skills. The individual in this position is expected to have practical experience in the design, development, management, and maintenance of systems as well as in handling large datasets. Expertise with commonly used AI and machine learning frameworks and a solid grasp of fundamental techniques—including deep learning, regression, classification, and clustering algorithms—are essential. Familiarity with retrieval-augmented generation pipelines and designing LLM-supported use cases is also critical.

Responsibilities

• Leading AI strategy by delivering integrated solutions that combine software engineering, statistics, and machine learning for complex clinical applications.
• Executing rigorous, analytical experiments to achieve incremental improvements using the most suitable techniques.
• Preparing comprehensive reports and presentations to convey hypotheses and insights that support organizational decision-making.
• Becoming the subject matter expert on available organizational data by identifying, collecting, transforming, and exploring datasets.
• Facilitating seamless collaboration between engineers, product analysts, and other teams within the organization.

Experience

• 5 to 7 years of relevant experience.
• Leadership experience is advantageous.
• At least three years of experience in an applied scientist role.
• Advanced degree (Ph.D. or MSc) in computer science, machine learning, AI, or related fields, or equivalent experience in designing, building, and evaluating machine learning systems.
• Proficiency with machine learning ecosystem tools such as PyTorch, TensorFlow, Scikit-learn, and XGBoost.
• Strong statistical analysis and machine learning knowledge, with practical programming skills (e.g., Python, R, SQL).
• Expertise in data visualization, model implementation, testing, and deployment.
• Experience evaluating and working with LLM-based pipelines, including retrieval-augmented generation and prompting techniques, is advantageous.
• Familiarity with tools like LangChain, LlamaIndex, Haystack, Azure AI Studio, vector databases, or equivalent LLM-enabled technologies.
• Proficiency in database access and management using SQL, Azure Data Factory, or similar technologies.
• Familiarity with big data frameworks (e.g., Hadoop, Spark) or equivalent systems.
• Understanding of MLOps principles, including orchestration tools, cloud computing, and observability platforms.