Senior Data Scientist
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Job Description
Senior Data Scientist building production ML models for FourKites’ AI-driven supply-chain visibility platform. Owning forecasting, NLP extraction, deployment, monitoring, and automation.
Responsibilities:
- Design, build, and productionize ML models for ETA/ATA prediction using regression, classification, and time-series forecasting
- Develop NLP/LLM-based extraction pipelines for message-based ETA and status updates
- Own models end-to-end from data pipeline through training, deployment, monitoring, and retraining
- Work with noisy, real-world logistics and supply chain data
- 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 using Grafana or similar tools
- Replace manual or rule-based processes with ML-driven automation
- Translate model performance improvements into operational savings, efficiency gains, and deal-relevant outcomes
- Mentor and guide other data scientists and engineers
- Make build-vs-buy and architecture tradeoff decisions independently
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
- Education is optional
- Experience in logistics, supply chain, or transportation is nice to have
- Familiarity with real-time/streaming data (Kafka) is nice to have
- Exposure to LLM/GenAI applications in production is nice to have
Benefits:
- Competitive compensation with stock options
- 5 global recharge days
- Generous PTO and standard holidays
- Parental leave for all parents
- Annual wellness stipend
- Volunteer days
- 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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