Product Manager, project44

• 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 two days weekly
• SQL (strong) and working knowledge of Python / notebooks / data analysis workflows
• Data platforms: Snowflake / BigQuery / Redshift / Databricks (Delta Lake)
• Orchestration & ETL: Airflow / Dagster / Prefect, dbt, Spark
• MLOps: experiment tracking/model registry (MLflow / Weights & Biases), pipelines (Kubeflow / SageMaker Pipelines / TFX / Flyte/ Tensor flow), serving (SageMaker / Vertex AI / Databricks Model Serving / KServe / BentoML)
• Streaming/event systems: Kafka / Kinesis / Flink
• Feature stores: Feast / Tecton / SageMaker Feature Store
• Monitoring/observability: Arize / WhyLabs / 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
• Even if you don’t meet 100% of the above qualifications, you should still seriously consider applying

Desirable

• 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
• You'll 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