Senior Data Scientist - Full Stack

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

1d•Hybrid

About The Position

Waste Management (WM) is seeking a senior, full-time Data Scientist to take complete ownership of analytics initiatives from problem definition through executive delivery. This role requires deep technical expertise and the ability to translate analysis into clear recommendations, anticipate stakeholder questions, and drive alignment independently. The position is hybrid, requiring presence in the Houston corporate office four days per week. The role involves designing, building, and evaluating advanced statistical, machine learning, AI, and GenAI models, performing advanced data mining, feature engineering, and analysis on large datasets, and translating model outputs into actionable insights. Collaboration with engineering and platform teams for production integration, and clear documentation of methodologies and results are also key aspects of this position.

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.
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

Knowledge and working experience in SAS toolsets (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.