Senior AI Data Scientist

Waste Management, Inc. (WM)•Houston, TX

1h•Onsite

About The Position

We are seeking a senior, full-stack Data Scientist with deep expertise in Deep learning, computer vision and agentic AI who can take complete ownership of analytics initiatives from problem definition, modeling lifecycle through executive delivery. The ideal candidate combines deep technical expertise with the ability to translate analysis into clear recommendations, anticipate stakeholder questions, and drive alignment without needing a manager to intermediate or interpret.

Requirements

Bachelor's Degree (accredited) or higher in Statistics, Applied Mathematics, Operations Research, Computer Science, or related fields.
5 years Expeof experience applying advanced analytics or data science in a business environment.

Nice To Haves

Master's Degree in Statistics, Applied Mathematics, Operations Research, Computer Science, or related fields.
Demonstrated experience owning projects independently and presenting to senior stakeholders
Ability to integrate RL with LLM based agents, including planning, tool use, memory, and feedback loops.
Experience in applying advanced reinforcement learning techniques including policy optimization, actor critic methods, offline RL, preference learning, and human in the loop feedback

Responsibilities

Own deep learning and agentic AI initiatives end to end, from problem framing and data exploration through modeling, validation, deployment, and measurement.
Partner directly with business and senior leaders to clarify objectives, constraints, and success criteria without relying on others to translate technical ideas.
Proactively identify opportunities to apply data science to business challenges.
Prepare and deliver executive-ready presentations that explain methodologies and recommendations, and present findings directly to stakeholders while answering questions in real time and defending technical decisions.
Independently manage priorities, scope, timelines, risks, and stakeholder expectations across multiple concurrent efforts.
Design, build, and evaluate deep learning models and agent based systems, selecting modeling approaches based on business needs, data constraints, and operational feasibility.
Perform advanced data mining, simulation, feature engineering, and analysis on large and complex datasets.
Translate model outputs into actionable, operational insights.
Ensure data quality, reliability, and reproducibility; clearly communicate risks and limitations.
Collaborate with engineering and platform teams to integrate models into production workflows.
Produce clear, well-structured documentation covering problem definitions, methodologies, assumptions, results, and recommendations.
Create artifacts (slide decks, summaries, dashboards, Confluence pages) that enable reuse without direct handholding.
Establish and follow best practices for analytical rigor and reproducibility.