Senior Data Scientist

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.
Responsibilities:
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, and 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 approaches and best practices.
Make build-vs-buy and architecture trade-off decisions independently.
Requirements:
Strong ML fundamentals across regression, classification, and time-series forecasting.
NLP experience in 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.

Experience
2-6 yrs