Benefits

Full Job Description

Role Overview This position is for a Senior Data Scientist to build and own production-grade machine learning solutions that solve complex, real-world supply chain problems. The role combines hands-on technical ownership with opportunities to guide other data scientists and engineers. You'll work with challenging, high-volume data including GPS signals, carrier information, and unstructured logistics messages. What You Will Do Design, develop, and productionize machine learning models for ETA/ATA prediction and other forecasting, regression, and classification problems. Build NLP and LLM-based extraction pipelines for unstructured messages, including text extraction and entity recognition for status and ETA information. Why It Might Be a Fit The ideal candidate will have strong foundations in machine learning, particularly regression, classification, and time-series forecasting. They will have proven experience building and deploying machine learning models that serve real production traffic and be able to work effectively with noisy, unstructured, or imperfect real-world data. Requirements Strong foundations in machine learning, particularly regression, classification, and time-series forecasting. Proven experience building and deploying machine learning models that serve real production traffic rather than remaining at proof-of-concept or notebook stage. Practical NLP experience involving text extraction, entity recognition, LLM-based extraction, or similar applications. Strong programming and data skills in Python and SQL, including experience with pandas, scikit-learn, and large datasets. Experience with cloud and data infrastructure, particularly AWS services such as S3 and EC2, with exposure to Airflow or similar orchestration platforms. Experience with model monitoring, observability, and production ML operations; familiarity with Grafana or comparable tools is valuable. Demonstrated ability to work effectively with noisy, unstructured, or imperfect real-world data. Experience diagnosing production model degradation and closing the gap between offline evaluation and live performance. A track record of using ML to automate manual or rule-based processes and deliver measurable improvements. Ability to connect technical model performance with business outcomes and communicate complex concepts clearly to non-technical stakeholders. Strong collaboration skills and experience partnering with product, engineering, and operations teams. Experience mentoring or providing technical guidance to other data scientists or engineers. Ability to independently evaluate architecture options and make effective build-versus-buy decisions. Excellent written and verbal communication skills. Experience in logistics, supply chain, transportation, real-time streaming data such as Kafka, or production LLM/GenAI applications is a plus. Benefits Competitive compensation package with stock options. Medical benefits starting from the first day of employment. 36 days of PTO covering sick, casual, and earned leave. 5 additional global recharge days and 2 volunteer days. Home-office setup and technology reimbursement. Lifestyle and family benefits. Mental wellness support and guidance. Ongoing learning and professional development opportunities, including development programs and communication-focused initiatives. Collaborative and inclusive work environment that values diversity and continuous learning.

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