Senior Data Science Engineer
Trinetix
full-time
Posted on:
Location Type: Remote
Location: Poland
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Job Level
About the role
- Build demand forecasting models (XGBoost, BSTS, Temporal Fusion Transformer) with hierarchical reconciliation across 545+ locations
- 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
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
Benefits
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Hard Skills & Tools
PythonXGBoostBSTSTemporal Fusion TransformerMILPMin-Cost FlowTextractLayoutLMv3SHAPLIME