Senior Data Scientist - Reinforcement Learning at WM

Houston, Texas, United States -

Full Time

Start Date

Immediate

Expiry Date

30 Jul, 26

Salary

0.0

Posted On

01 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Reinforcement Learning, Computer Vision, Agentic AI, Python, SQL, Snowflake, AWS, Microsoft Azure, Data Mining, Statistical Modeling, Machine Learning, Simulation, Feature Engineering, Jira, Confluence, Data Visualization

Industry

Environmental Services

Description

Waste Management (WM), a Fortune 250 company, is the leading provider of comprehensive waste and environmental services in North America. We are strongly committed to a foundation of operating excellence, professionalism and financial strength. WM serves nearly 25 million customers in residential, commercial, industrial and municipal markets throughout North America through a network of collection operations, transfer stations, landfills, recycling facilities and waste-based energy production projects. To enable our business to expand our lead in a market increasingly enhanced by technology, Waste Management is undertaking a substantial technology transformation. We are seeking talented Information Technology professionals to join the Waste Management team who are motivated to help us transform the way we design, build and use technology. With your skills and experience, we look for you to combine your technical expertise with industry best practices in an effort to align information technology solutions with Waste Management business strategy. I. Job Summary We are seeking a senior, full-stack Data Scientist with deep expertise in Reinforcement Learning (RL), 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. II. Essential Duties and Responsibilities To perform this job successfully, an individual must be able to perform each duty satisfactorily. Other ancillary duties may be assigned. * Project Ownership and Stakeholder Communication * 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. * Modeling and Technical Execution * 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. * Documentation & Knowledge Sharing * 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. * Technical Skills * 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). * What Sets Successful Candidates Apart * 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. III. Supervisory Responsibilities 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. IV. Qualifications The requirements listed below are representative of the qualifications necessary to perform the job. A. Education and Experience * Education: 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. * Experience: Five years of relevant work experience (in addition to education requirement). B. Preferred Qualifications * 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. Certificates, Licenses, Registrations or Other Requirements * None required. C. Other Knowledge, Skills or Abilities Required Advanced knowledge and skills in one or more of the following is required: * 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. V. Work Environment Listed below are key points regarding environmental demands and work environment of the job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions of the job. Normal setting for this job is: office setting Benefits At Waste Management, each eligible employee receives a competitive total compensation package including Medical, Dental, Vision, Life Insurance and Short Term Disability. As well as a Stock Purchase Plan, Company match on 401K, and more! Our employees also receive Paid Vacation, Holidays, and Personal Days. Please note that benefits may vary by site. If this sounds like the opportunity that you have been looking for, please click "Apply.� ABOUT WM WM (WM.com [https://www.wm.com]) is North America's leading provider of comprehensive environmental solutions. Previously known as Waste Management and based in Houston, Texas, WM is driven by commitments to put people first and achieve success with integrity. The company, through its subsidiaries, provides collection, recycling and disposal services to millions of residential, commercial, industrial, medical and municipal customers throughout the U.S. and Canada. With innovative infrastructure and capabilities in recycling, organics and renewable energy, WM provides environmental solutions to and collaborates with its customers in helping them pursue their sustainability goals. WM has the largest disposal network and collection fleet in North America, is the largest recycler of post‑consumer materials and is a leader in beneficial use of landfill gas, with a growing network of renewable natural gas plants and the most landfill gas‑to‑electricity plants in North America. WM’s fleet includes more than 12,000 natural gas trucks – the largest heavy‑duty natural gas truck fleet in the industry in North America. Healthcare Solutions provides collection and disposal services of regulated medical waste, as well as secure information destruction services, in the U.S., Canada and Western Europe. To learn more about WM and the company’s sustainability progress and solutions, visit Sustainability.WM.com [https://sustainability.wm.com/]. Equal Employment Opportunity For United States: WM is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity, national origin, age, disability, status as a protected veteran or any other characteristic protected under applicable federal, state, or local law. For Canada: WM is committed to the principle of equal employment for all applicants and employees, without discrimination on the basis of all grounds protected by applicable human rights legislation. Accommodations are available on request for candidates taking part in all aspects of the selection process. Please notify us if you require accommodation. Real ID In order to travel by air or access federal property, federal law requires individuals have a REAL ID or an acceptable alternative. This position may require the successful candidate to travel by air for business reasons or service federal property. Accordingly, successful candidates must have, or be willing to obtain, a REAL ID or TSA‑approved alternative. What is the value of a WM job? At WM we know that the value of a WM job is more than a paycheck. It’s a way to create opportunities for you and your family. This is why we are constantly working to make WM a great place to work and grow a career. We Are WM is what defines the perks of being in the WM family – from benefits, to resources and engagement activities. We are People First. We are Committed to Your Growth. We Are Investing in You. We are a Family. We are Stable. We are Always Working for a Sustainable Tomorrow.

Responsibilities

The Senior Data Scientist will own reinforcement learning and agentic AI initiatives from problem definition through deployment and executive delivery. They will partner with business leaders to translate complex technical findings into actionable recommendations and operational insights.