Product Manager, Data Science Platform; AI​/ML

We’re hiring a technical, hands‑on Product Manager to scale ML‑powered products (ETA, risk, anomalies/exceptions, network insights) while building Data Science as a Platform—reusable capabilities that accelerate model development, deployment, monitoring, and governance across multiple product areas.

You’ll partner daily with Data Science, ML Engineering, Data Engineering, Platform Engineering, and Product teams to ship production ML and establish repeatable, measurable ML delivery.

• Own the roadmap for applied ML capabilities beyond ETA (risk scoring, exception prediction, anomaly detection, carrier/network performance insights), from discovery to launch and iteration.
• Define and deliver a DS/ML platform: feature management, experimentation, model registry, deployment patterns, monitoring/observability, governance, and self‑serve tooling.
• Translate customer workflows into ML problem statements: labels/targets, constraints, SLAs, interpretability, and “do no harm” launch gates.
• Drive evaluation and experimentation: offline metrics/back testing, online testing (A/B, holdouts), and measurable business impact.
• Partner with engineering on batch + real‑time inference architectures, streaming/event‑time feature needs, and reliability (SLOs, incident playbooks).
• Establish and track platform success metrics: time‑to‑first‑model, deployment frequency, reuse rate, model performance stability, incident rate, and ROI.

• 5–8+ years Product Management experience with 3–4+ years focused on DS/ML‑driven products (or equivalent).
• Demonstrated ability shipping end‑to‑end ML systems to production and iterating based on outcomes.
• Strong technical fluency across the ML lifecycle (data → features → training → serving → monitoring → retraining).
• Ability to operate between Sr and Principal: sets direction, aligns stakeholders, and drives execution across multiple teams.
• Excellent written communication (PRDs, decision docs) and crisp cross‑functional leadership.
• In‑office Commitment:
  Employees are expected to contribute to our collaborative culture by working in the office Four days weekly.

• SQL (strong) and working knowledge of Python / notebooks / data analysis workflows.
• Data platforms:
  Snowflake / Big Query / Redshift / Databricks (Delta Lake).
• Orchestration & ETL:
  Airflow / Dagster / Prefect, dbt, Spark.
• Streaming/event systems:
  Kafka / Kinesis / Flink.
• MLOps: experiment tracking/model registry (MLflow / Weights & Biases), pipelines (Kubeflow / Sage Maker Pipelines / TFX / Flyte / Tensor flow), serving (Sage Maker / Vertex AI / Databricks Model Serving / KServe / Bento
  
  ML).
• Feature stores:
  Feast / Tecton / Sage Maker Feature Store.
• Monitoring/observability:
  Arize / Why Labs / Evidently + Prometheus / Grafana / Datadog.
• Platform fundamentals:
  Docker / Kubernetes, API design (REST / gRPC), SLAs/SLOs, security & PII basics.
• LLMs/GenAI (RAG, embeddings, vector DBs like Pinecone / Weaviate / Milvus), evaluation + guardrails.

• Experience in logistics / supply chain / transportation or other high‑volume operational domains.
• Familiarity with geospatial/event‑time data, carrier APIs/EDI, entity resolution, and enterprise exception workflows.
• Platform product experience with internal “developer” customers and adoption metrics.

We’re committed to creating a company where anyone can bring their authentic self to work every day. We are an equal opportunity employer and actively work to create a diverse and inclusive work environment where underrepresented groups can thrive.

For any needed accommodations during the hiring process, please email  Even if you don’t meet 100% of the above qualifications, you should still seriously consider applying. Studies show that you can still be considered for a role if you meet just 50% of the role’s requirements.