Senior Data Scientist - Full Stack

WM•Houston, TX

1d•Hybrid

About The Position

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. We are seeking a senior, full-time Data Scientist who can take complete ownership of analytics initiatives from problem definition 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. This role will be hybrid, present 4 days per week in the Houston corporate office.

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, AI, and GenAI techniques.
Strong programming skills in Python and/or R.
Advanced SQL and experience with large-scale data platforms Snowflake, PostgreSQL, DataStax/Astra DB).
Cloud and data science platforms (AWS, S3, Spark, SageMaker).
Data visualization and storytelling tools.
Agile tools (Jira, Confluence).
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

SAS training preferred

Responsibilities

Own analytics 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 advanced statistical, machine learning, AI, and GenAI models, selecting modeling approaches based on business needs, data constraints, and operational feasibility.
Perform advanced data mining, 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 hand-holding.
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.