Data Scientist / ML Engineer

THEMIS Waste Recovery Technology

Data Scientist / ML Engineer turning data into intelligence for governance, risk, and compliance. Collaborating across the full data lifecycle and applying ML to real workflows.

Posted 6/26/2026full-timeRemote • New York • 🇺🇸 United StatesMid-LevelSeniorWebsite

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Hard Skills

machine learningstatisticsdata sciencePythonpandasscikit-learnPyTorchTensorFlowSQLdata modeling

Soft Skills

strong communication skillsability to manage ambiguityproblem ownershipcollaborationclear recommendationsexperiment designmetrics definitionrigorous evaluationexplainabilitybias considerations

Tools & Technologies

Themis platformdata pipelinesML pipelinesAI featuresproduction monitoringdata qualitydata integritycustomer workflowsmodel deploymentdata exploration

Industry Keywords

compliancerisk managementregulated domaindata productsmodel performancehigh-value opportunitiesLLM-powered featuresreal-world datadata preparationmodel integration

Tech Stack

Tools & technologies

PandasPythonPyTorchScikit-LearnSQLTensorflow

About the role

Key responsibilities & impact

Frame ambiguous compliance and risk problems as well-defined data and modeling tasks
Build, evaluate, and iterate on machine learning models and LLM-powered features
Design experiments and define metrics that measure real impact on customer workflows
Apply rigorous evaluation, including accuracy, explainability, and bias considerations appropriate to a regulated domain
Build and maintain data and ML pipelines for training, inference, and monitoring
Deploy models and AI features into production and monitor their performance over time
Collaborate with engineering to integrate models into the Themis platform reliably and at scale
Explore and prepare data, build features, and ensure data quality and integrity
Translate data and model findings into clear recommendations for product and leadership
Partner with Product to identify high-value opportunities for ML and AI

Requirements

What you’ll need

Strong foundation in machine learning, statistics, and data science fundamentals
Proficiency in Python and common data and ML libraries (e.g., pandas, scikit-learn, PyTorch, or TensorFlow)
Experience taking models or data products from prototype to production
Experience with SQL and working with real-world, messy data
Ability to design experiments, define metrics, and evaluate models rigorously
Strong communication skills and the ability to explain technical work to non-technical stakeholders
Ability to manage ambiguity and own problems end to end

Benefits

Comp & perks

Flexible working hours
Remote work options