Senior Data Scientist - Reinforcement Learning

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

14h•Onsite

About The Position

Waste Management (WM) is undergoing a significant technology transformation and is seeking talented Information Technology professionals to join their team. This role is for a senior, full-stack Data Scientist with deep expertise in Reinforcement Learning (RL), computer vision, and agentic AI. The individual will take complete ownership of analytics initiatives, from problem definition and the modeling lifecycle through to executive delivery. The ideal candidate will combine deep technical expertise with the ability to translate analysis into clear recommendations, anticipate stakeholder questions, and drive alignment independently.

Requirements

Bachelor's degree (accredited) in Economics, Applied Mathematics, Computer Science, or similar area of study, or in lieu of degree, High School Diploma or GED and 4 years of relative work experience.
Five years of relevant work experience (in addition to education requirement).
Advanced statistical, machine learning, computer vision, AI, GenAI, and agentic AI techniques.
Strong background in reinforcement learning and sequential decision making.
Strong programming skills in Python
Advanced SQL and experience with large-scale data platforms such as Snowflake.
Cloud and data science platforms such as AWS and Microsoft Azure.
Data visualization and storytelling tools.
Agile tools (Jira, Confluence).
Deep technical mastery of reinforcement learning, machine learning, and agent based systems, with the ability to design, critique, and improve models beyond standard recipes.
Strong applied problem solving skills, including translating ambiguous business objectives into well defined learning problems, environments, and success metrics.
Proven ability to diagnose model behavior, identify failure modes, and iteratively improve performance using data, experimentation, and sound theory.
Hands on experience working across the full modeling lifecycle, from data exploration and feature design through training, evaluation, and production integration.
Ability to defend modeling choices and assumptions with rigor, including trade offs between accuracy, robustness, interpretability, and operational constraints.
Strong engineering mindset, with attention to reproducibility, experiment tracking, data quality, and technical documentation.
Exceptional written and verbal communication skills.
Ability to operate autonomously and drive work forward without supervision.
Comfort presenting to senior leadership and defending assumptions.
Strong documentation and follow-through habits.
Business-oriented mindset balancing rigor with practicality.
Knowledge and understanding in how to identify root causes of problems, create effective practical solution approaches, and implement solutions under the tactical demands of business operations.
Experience leading and working as part of a integrated solutions development team to provide value to systems engineering and development for specific decision support application.
Experience working with large-scale data sets in an advanced data mining analytic role.
Practical knowledge and demonstrated experience of statistical models and methods.
Knowledge of large relational databases, and SQL programming.
Knowledge and working experience in SAS toolsets (SAS training preferred).
Programming experience (preferably in C or C#).
Problem solving and analytical skills.
Ability to present, communicate and articulate complex information to all levels of the organization (including technical and non-technical audiences, Senior Leadership and Executive Leadership).
Committed and highly motivated team player.
Ability to demonstrate a customer service and customer focused mindset.
Proficiency with data mining and visualization tools.

Nice To Haves

Master’s degree or higher in Statistics, Applied Mathematics, Operations Research, Computer Science, or related fields.
5+ years of experience applying advanced analytics or data science in a business environment.
Demonstrated experience owning projects independently and presenting to senior stakeholders.

Responsibilities

Own reinforcement 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 reinforcement learning models and agent based systems, selecting modeling approaches based on business needs, data constraints, and operational feasibility.
Apply advanced techniques including policy optimization, actor critic methods, offline RL, preference learning, and human in the loop feedback.
Integrate RL with LLM based agents, including planning, tool use, memory, and feedback loops.
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.
May coach and mentor less-experienced personnel and act as team leader on systems projects, possibly requiring up to 30% of time spend performing duties and responsibilities.