Data Scientist

What you'll be doing:

• Design, build, and productionize ML models for problems like ETA/ATA prediction, using regression, classification, and time-series forecasting techniques
• Develop NLP/LLM-based extraction pipelines for message-based ETA and status updates (text extraction, entity recognition)
• Own models end-to-end: data pipeline → training → deployment → monitoring → retraining
• Work with noisy, real-world logistics and supply chain data (GPS pings, check calls, carrier data) rather than clean, pre-processed datasets
• Diagnose gaps between offline evaluation performance and live production accuracy, and drive fixes
• Build and maintain automated training/retraining pipelines using orchestration tools such as Airflow
• Set up and maintain model monitoring and observability (e.g., Grafana) to catch drift and degradation proactively
• Replace manual or rule-based processes with ML-driven automation (e.g., automating manual check calls)
• Translate model performance improvements into business impact — operational savings, efficiency gains, and deal-relevant outcomes
• Mentor and guide other data scientists/engineers on technical approach and best practices
• Make build-vs-buy and architecture tradeoff decisions independently

About the team:

Our product and engineering teams are dedicated to providing the industry's best-in-class end-to-end supply chain visibility platform. We are committed to building a high-performing, ML-driven team that turns supply chain data into automated, proactive action — and we want you to help lead that effort.

Who you are:

• Strong ML fundamentals across regression, classification, and time-series forecasting
• NLP experience — text extraction, entity recognition, or LLM-based extraction
• Production ML experience — you've shipped models serving real traffic, not just built POCs or notebooks
• Strong Python and SQL skills — pandas, scikit-learn, and comfort querying large datasets (Redshift/Snowflake a plus)
• Experience with cloud and data infrastructure — AWS (S3, EC2), and orchestration tools like Airflow for training/retraining pipelines
• Experience setting up or working with model monitoring and observability tooling (Grafana or similar)
• Comfortable working with noisy, real-world data rather than clean, curated datasets
• Experience diagnosing and closing the gap between offline evaluation results and live production performance
• A track record of replacing manual/rule-based processes with ML solutions
• Ability to translate model output into business value and communicate that impact to non-technical stakeholders
• Experience collaborating cross-functionally with product, engineering, and operations teams
• Experience mentoring or guiding other data scientists or engineers
• Ability to make build-vs-buy and architecture tradeoffs independently
• A track record of reducing manual intervention or turnaround time through automation
• Excellent oral and written communication skills

Nice to have:

• Experience in logistics, supply chain, or transportation
• Familiarity with real-time/streaming data (Kafka)
• Exposure to LLM/GenAI applications in production

Benefits:

• Medical benefits start on first day of employment
• 36 PTO days (Sick, Casual and Earned), 5 recharge days, 2 volunteer days
• Home Office set ups and Technology reimbursement
• Lifestyle & Family benefits
• Mental Wellness support and guidance
• Ongoing learning & development opportunities (Professional development program, Toast Master club, etc.)