3 months ago
Bengaluru, IndiaStaff+
Responsibilities
- Lead end-to-end development of ML models for ETA, risk, anomaly detection, and fraud using techniques such as embeddings, transformers, and hybrid models.
- Build reusable ML infrastructure for features, experimentation, deployment, and monitoring to scale model development and reduce time to production.
- Develop LLM-powered solutions, RAG systems, diagnostics, automation, coding agents, and agentic workflows with appropriate guardrails and evaluation.
- Translate customer workflows and business problems into measurable ML solutions and establish offline and online evaluation frameworks.
- Collaborate with Data Science, ML Engineering, Data Engineering, Platform, and Product teams to deliver scalable, reliable systems across the ML lifecycle.
- Deliver high-impact ML capabilities, standardize experimentation and monitoring workflows, increase reuse across data science teams, and improve model performance and system reliability.
Requirements
- 8–10+ years of experience in data science or applied machine learning, with a track record of building and deploying production-grade ML systems.
- Deep expertise in tree-based models, transformers, probabilistic modeling, and feature engineering.
- Proficiency in Python and SQL, with hands-on experience using Snowflake or Databricks, Spark, Airflow, Kafka, and MLOps tooling.
- Experience building RAG systems, working with embeddings and vector databases, and developing LLM-based applications and agentic workflows.
- Understanding of evaluation, guardrails, explainability, distributed systems, APIs, deployment patterns, and production-quality code.
- Ability to evaluate model performance, detect edge cases, conduct root-cause analysis, and build monitoring and evaluation frameworks.
- Experience addressing data drift, bias, missingness, anomalies, and other issues in messy real-world data.
- Ability to choose pragmatically between ML and heuristics and balance accuracy, scalability, latency, and customer experience.
- Strong problem framing, stakeholder influence, communication, and demonstrated measurable business impact.
- Preferred experience in logistics, supply chain, high-volume operational systems, geospatial data, routing, tracking, anomaly detection, fraud modeling, ETA prediction, or internal data science platforms.
Benefits
- Work in-office three days per week.
- Collaborate with a global, diverse team focused on logistics and supply chain innovation.
- Equal opportunity employer with accommodation support during the hiring process.
Tech Stack
Categories
About project44
project44 builds an enterprise SaaS platform that gives shippers, retailers, and logistics providers real-time visibility, predictive insights, and workflow tools across global transportation networks. Its products span end‑to‑end shipment tracking, intelligent transportation management, yard operations, last‑mile delivery, and developer APIs that integrate with TMS/ERP systems. Founded in 2014 and headquartered in Chicago, the privately held company serves customers across manufacturing, automotive, retail, life sciences, and other complex supply chain industries.