Benefits

Full Job Description

Role Overview As a Senior Data Scientist, you will build and own machine learning models that power core prediction problems across the FourKites platform. 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. What You Will Do 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). Why It Might Be a Fit 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. You will translate model performance improvements into business impact — operational savings, efficiency gains, and deal-relevant outcomes. Requirements 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 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.)

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