Sr Data Scientist

At FourKites we have the opportunity to tackle complex challenges with real-world impacts. Whether its medical supplies from Cardinal Health or groceries for Walmart, the FourKites platform helps customers operate global supply chains that are efficient, agile and sustainable.

Join a team of curious problem solvers that celebrates differences, leads with empathy and values inclusivity.

As a Senior Data Scientist, you will build and own machine learning models that power core prediction problems across the FourKites platform - including ETA/ATA forecasting and message-based status extraction. You will work end-to-end, from data pipeline to production deployment and monitoring, turning noisy real-world logistics data into models that run at scale and directly move the needle on customer outcomes. You will work closely with product, engineering, and operations teams, hands-on building and shipping models yourself while also guiding the technical direction of other data scientists on the team.

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

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 - youve 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

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.