SDS 479: Knowledge Graphs — with Maureen Teyssier

Candidate Qualities

Maureen Teyssier emphasizes the importance of learning agility and communication when hiring data scientists. She values candidates who are reflective and can evaluate data before jumping to conclusions, akin to a detective's mindset 1. This approach ensures that data scientists can navigate complex and messy data effectively. Additionally, Maureen highlights the significance of writing clean and understandable code, which she considers another form of communication 2.

We look for people that are keen to learn and keen to grow, and we look for people that have...that Sherlock Holmes instinct.

--- Maureen Teyssier

This skill is crucial for collaboration between data scientists and engineers, ensuring seamless integration of AI and ML within pipelines.

   

Team Roles

The roles within a data science team are distinct yet interconnected. Maureen distinguishes between data analysts, who focus on identifying opportunities and insights, and data scientists, who build and validate models 3. She also explains the role of machine learning engineers, who are primarily concerned with deploying models, while data engineers manage the pipelines that support data ingestion and transformation 4.

The MLEs are focused really on the models, those components that fit within the pipeline.

--- Maureen Teyssier

This clear delineation of roles ensures that each team member contributes effectively to the overall data science process.