Principal Scientist, Data Science

Key Skills

Azure, Causal inference, Google Cloud Platform (GCP), JIRA, Large scale data processing, Python Programming

Job Description

What you'll do:

• Leverage generative AI to create insights and explain the task assignment and/or prioritization to end users.

• Apply broad knowledge of ML/AI capabilities to identify growth areas, new and emerging opportunities. Conducts research by studying organization goals, strategies, practices, and user projects. Recommends strategies by evaluating organization outcomes; identifying problems; evaluating trends; anticipating requirements.

• Influence a team of Data Scientists focused on powering AI/ML solutions within the scope of associate work prioritization, workforce planning and retention strategies.

• Partner closely with Data Science Management, Business Leaders, and ML engineering leaders to inform, drive and accelerate innovations in discovery experiences via Insights, frameworks, causal inference solutions and machine learning prototypes.

• Execute full life cycle of projects through project planning, data collection, model prototyping and deployment, with responsibilities encompassing stakeholder management and communication to cross-functional partners.

• Adhere to MLOps framework to create, build, and deploy data science algorithms continuously test and monitor drifts.

• Shares highly complex information related to areas of expertise. Interacts with senior management to keep abreast of objectives. Interacts with peers to interpret information and improve cross-functional processes and programs. Builds and enhances key internal and external contacts.

What you'll bring:

• Experience in the development of large-scale ML/AI solutions, Causal Inference and Ranking/Prioritization systems.

• 6 years+ of experience in Applied ML familiar with core supervised and unsupervised algorithms.

• Skilled in Programming language (Python) and Large-scale training using data structures and algorithms.

• 2 years+ experience with Causal Inference frameworks, Bayesian Networks and Operations Research methods (Newsvendor problem, Travelling Salesman problem, Shortest Path models, etc.)

• Experience with cloud environments including Azure, Google Cloud, etc.

• Solid MLOps practices including good design documentation, unit testing, integration testing and source code control (git).

• Experienced with agile methodologies using project planning and tracking management tools e.g., JIRA.