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Job details
We are seeking a skilled Data Science Engineer to design, build, and deploy production machine learning solutions for an enterprise Fleet Cascading Optimization Platform managing 46,000+ vehicles across 545+
location
s.
In this role, you will develop and operationalize demand forecasting, cascading optimization, contract intelligence (NLP/Vision), and out-of-spec prediction models with a strong focus on explainability and business impact.
You will own the end-to-end ML lifecycle — from experimentation and model development to scalable production deployment on AWS—working closely with engineering and business stakeholders to deliver reliable, data-driven outcomes. Must-Have
Requirements
Programming ML Frameworks: Python; PyTorch or TensorFlow; scikit-learn; XGBoost or LightGBM; pandas; NumPy Time Series Forecasting: BSTS; Prophet; Temporal Fusion Transformer (TFT); hierarchical forecasting with MinT reconciliation Optimization: Linear Programming and MILP using tools such as PuLP and OR-Tools; constraint satisfaction; min-cost flow optimization AWS ML Stack: Amazon SageMaker (Training Jobs, Endpoints, Model Monitor, Clarify, Feature Store, Pipelines) Nice-to-have NLP Document AI: Amazon Textract; LayoutLMv3; Retrieval-Augmented Generation (RAG) pipelines; Amazon Bedrock (Claude); OpenSearch vector databases Advanced Machine Learning: Graph Neural Networks (GNNs); Deep Reinforcement Learning; Survival Analysis (Cox Proportional Hazards, XGBoost-Survival); attention-based models Explainability MLOps: SHAP, LIME, Captum; MLflow; A/B testing; champion/challenger frameworks; model and data drift detection Core
Responsibilities:
Build demand forecasting models (XGBoost, BSTS, Temporal Fusion Transformer) with hierarchical reconciliation across 545+
location
s Develop cascading optimization using MILP/Min-Cost Flow solvers (PuLP, OR-Tools, Gurobi) and Hybrid ML+Optimization pipelines Implement document intelligence pipeline: Textract + LayoutLMv3 for document extraction, RAG with Bedrock (Claude) for semantic reasoning Deploy models on SageMaker with MLOps (Model Monitor, Feature Store, Pipelines); implement SHAP/LIME explainability Models You’ll Build Demand Forecasting: Gradient-boosted models (XGBoost), Bayesian Structural Time Series (BSTS), and Temporal Fusion Transformers (TFT), including hierarchical reconciliation Cascading Optimization: Mixed-Integer Linear Programming (MILP) and Min-Cost Flow models, evolving to hybrid ML + solver approaches and advanced Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) solutions Document Intelligence: Automated document extraction using Amazon Textract and LayoutLMv3, advancing to Retrieval-Augmented Generation (RAG) pipelines with Amazon Bedrock and Vision-Language Models Survival Out-of-Spec Prediction: Kaplan–Meier estimators, Cox Proportional Hazards models, and XGBoost-Survival techniques What we offer Continuous learning and career growth opportunities Professional training and English/Spanish language classes Comprehensive medical insurance Mental health support Specialized
benefits
program with compensation for fitness activities, hobbies, pet care, and more Flexible working hours Inclusive and supportive culture
About Us
Established in 2011, Trinetix is a dynamic tech service provider supporting enterprise clients around the world. Headquartered in Nashville, Tennessee, we have a global team of over 1,000 professionals and delivery centers across Europe, the United States, and Argentina.
We partner with leading global brands, delivering innovative digital solutions across Fintech, Professional Services, Logistics, Healthcare, and Agriculture.
Our operations are driven by a strong business vision, a people-first culture, and a commitment to responsible growth.
We actively give back to the community through various CSR activities and adhere to international principles for sustainable development and business ethics. To learn more about how we collect, process, and store your personal data, please review our Privacy Notice: https://www. trinetix. com/corporate-policies/privacy-notice.
location
s.
In this role, you will develop and operationalize demand forecasting, cascading optimization, contract intelligence (NLP/Vision), and out-of-spec prediction models with a strong focus on explainability and business impact.
You will own the end-to-end ML lifecycle — from experimentation and model development to scalable production deployment on AWS—working closely with engineering and business stakeholders to deliver reliable, data-driven outcomes. Must-Have
Requirements
Programming ML Frameworks: Python; PyTorch or TensorFlow; scikit-learn; XGBoost or LightGBM; pandas; NumPy Time Series Forecasting: BSTS; Prophet; Temporal Fusion Transformer (TFT); hierarchical forecasting with MinT reconciliation Optimization: Linear Programming and MILP using tools such as PuLP and OR-Tools; constraint satisfaction; min-cost flow optimization AWS ML Stack: Amazon SageMaker (Training Jobs, Endpoints, Model Monitor, Clarify, Feature Store, Pipelines) Nice-to-have NLP Document AI: Amazon Textract; LayoutLMv3; Retrieval-Augmented Generation (RAG) pipelines; Amazon Bedrock (Claude); OpenSearch vector databases Advanced Machine Learning: Graph Neural Networks (GNNs); Deep Reinforcement Learning; Survival Analysis (Cox Proportional Hazards, XGBoost-Survival); attention-based models Explainability MLOps: SHAP, LIME, Captum; MLflow; A/B testing; champion/challenger frameworks; model and data drift detection Core
Responsibilities:
Build demand forecasting models (XGBoost, BSTS, Temporal Fusion Transformer) with hierarchical reconciliation across 545+
location
s Develop cascading optimization using MILP/Min-Cost Flow solvers (PuLP, OR-Tools, Gurobi) and Hybrid ML+Optimization pipelines Implement document intelligence pipeline: Textract + LayoutLMv3 for document extraction, RAG with Bedrock (Claude) for semantic reasoning Deploy models on SageMaker with MLOps (Model Monitor, Feature Store, Pipelines); implement SHAP/LIME explainability Models You’ll Build Demand Forecasting: Gradient-boosted models (XGBoost), Bayesian Structural Time Series (BSTS), and Temporal Fusion Transformers (TFT), including hierarchical reconciliation Cascading Optimization: Mixed-Integer Linear Programming (MILP) and Min-Cost Flow models, evolving to hybrid ML + solver approaches and advanced Graph Neural Network (GNN) and Deep Reinforcement Learning (DRL) solutions Document Intelligence: Automated document extraction using Amazon Textract and LayoutLMv3, advancing to Retrieval-Augmented Generation (RAG) pipelines with Amazon Bedrock and Vision-Language Models Survival Out-of-Spec Prediction: Kaplan–Meier estimators, Cox Proportional Hazards models, and XGBoost-Survival techniques What we offer Continuous learning and career growth opportunities Professional training and English/Spanish language classes Comprehensive medical insurance Mental health support Specialized
benefits
program with compensation for fitness activities, hobbies, pet care, and more Flexible working hours Inclusive and supportive culture
About Us
Established in 2011, Trinetix is a dynamic tech service provider supporting enterprise clients around the world. Headquartered in Nashville, Tennessee, we have a global team of over 1,000 professionals and delivery centers across Europe, the United States, and Argentina.
We partner with leading global brands, delivering innovative digital solutions across Fintech, Professional Services, Logistics, Healthcare, and Agriculture.
Our operations are driven by a strong business vision, a people-first culture, and a commitment to responsible growth.
We actively give back to the community through various CSR activities and adhere to international principles for sustainable development and business ethics. To learn more about how we collect, process, and store your personal data, please review our Privacy Notice: https://www. trinetix. com/corporate-policies/privacy-notice.
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