Senior Data Scientist (Decision Intelligence)

• Work directly with production data, factory operations, and charging station network data to identify bottlenecks that can be solved with AI/Machine Learning, and estimate the economic value before building anything.

• Build, test, and deploy models for forecasting, classification, anomaly detection, and decision support on time-series, tabular, and equipment sensor data.

• Develop models to support production optimization, predictive maintenance, demand forecasting, load/energy forecasting, and equipment operational health assessment.

• Combine forecasting models with Optimization, Scheduling, and Routing algorithms into a complete decision-making pipeline: forecast outputs and their error margins serve as inputs to the optimization problem, with operational efficiency—not model accuracy—as the ultimate success metric.

• Develop AI Services and integrate models into internal systems/platforms, ensuring latency, throughput, and computational cost constraints are met in production.

• Collaborate with Optimization, Data, Software, and business teams to clarify requirements and real-world constraints, and deploy/evaluate solutions directly with end users.

• Monitor model performance post-deployment (accuracy, stability, and efficiency); automate retraining and operational workflows.

• 5+ years of experience as a Data Scientist / ML Engineer / OR Engineer, including at least one ML or optimization system that has been deployed in production and delivered measurable impact.

• Degree in Applied Mathematics, Data Science, Computer Science, Econometrics, Industrial Engineering/Operations Research, or a related field; strong foundation in Machine Learning, Statistics, Probability, and Data Structures & Algorithms.

• Hands-on experience with Time Series Forecasting, Classification/Regression, Anomaly Detection, or Predictive Maintenance; proficient in Python (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM) and SQL.

• Able to formulate optimization problems as LP/MILP/CP and proficient with at least one solver (Gurobi, CPLEX, OR-Tools, HiGHS, Pyomo); skilled in applying meta-heuristics (GA, SA, Tabu, ALNS) for large-scale problems that exact solvers cannot solve within an acceptable timeframe.

• Familiar with real-world operational problems: Scheduling, Routing, Inventory Control, Assignment.

• Able to connect forecasting outputs to optimization problems into a complete decision-making pipeline, using operational efficiency as the measure of model success.

• Strong hands-on capability; understands the full pipeline from experimentation to Production (Docker, CI/CD, model versioning, monitoring) and can work with data integrated from multiple sources.

• Strong modeling mindset: able to translate a business problem into one with clear objectives, constraints, and evaluation criteria.

Preferred

• Experience in manufacturing, supply chain, energy, EV/charging stations, logistics, or industrial systems.

• Advanced OR knowledge: Graph Theory, Network Flow, Column Generation, Stochastic/Robust Optimization; Discrete-Event Simulation or Digital Twin.

• Geospatial data (PostGIS, H3, OSRM) and international logistics practices (Incoterms, DEM/DET, S&OP).

• FastAPI, MLflow, Airflow, Kubernetes, Cloud (AWS/GCP/Azure); vector databases (FAISS, Milvus, Qdrant, pgvector).

• National/Olympiad awards in Mathematics, Informatics, or AI; achievements in Data Science, Machine Learning, or Optimization competitions.