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Remote senior ML role at a known enterprise with broad ML/NLP requirements attracts many qualified applicants.
Production ML and NLP skills are transferable, though logistics domain experience increases hiring preference.
Requires production ML, NLP, cloud, orchestration, monitoring and mentoring experience, enforcing strict technical filters.
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Design, build, and maintain end-to-end machine learning models for core logistics problems including ETA/ATA prediction and message-based status extraction using NLP/LLM techniques.
Develop and operate automated training, retraining, deployment, and monitoring pipelines for large-scale, noisy supply chain data using orchestration and observability tools like Airflow and Grafana.
Lead technical direction of ML projects, mentor other data scientists, and independently make architecture and build-vs-buy tradeoff decisions, driving measurable business impact through model improvements and automation.
Work Experience Required: Not explicitly mentioned in the JD.
Strong proficiency in ML techniques including regression, classification, time-series forecasting, and NLP (text extraction, entity recognition, LLM-based extraction).
Experience with production ML systems: deployment, monitoring, and retraining pipelines using tools such as Airflow and Grafana.
Strong skills in Python, SQL, cloud infrastructure (AWS services like S3, EC2), and working with large, noisy real-world datasets from supply chain/logistics domain.
Experienced in end-to-end deployment and operational management of ML models in production environments involving complex, noisy logistics data.
Demonstrated ability to close gaps between offline model evaluation and live production performance, and translate ML outputs into tangible business outcomes and efficiencies.
Someone who can independently guide technical directions, make tradeoff decisions around architecture and build vs buy, and mentor junior data scientists within cross-functional teams.