Lead - Data Science & AI Engineering

Job Description

Role ShipDelight is building the next generation of logistics intelligence where on-ground operations are increasingly driven by data, prediction and recommended action rather than manual interventions. Our target operating model is : Data → Detection → Context → Reason → Impact → Recommended Action → Intervention → Closure. We are looking for a hands-on Lead – Data Science & AI Engineering who can bridge numerical data science, production engineering, MLOps/DevOps and logistics domain understanding. This person should be equally comfortable building a statistical/model-driven solution, understanding how it will run reliably in production and translating a real operations problem into measurable variables and decisions. What we are looking for 4–5 years of hands-on experience in Data Science / Machine Learning / AI Engineering. Strong Python skills and experience with numerical/tabular datasets. Strong understanding of statistics, probability, regression/classification, tree-based models, anomaly detection, forecasting and scoring systems. Experience deploying ML models into production rather than only experimentation. Working knowledge of AWS/cloud infrastructure, Docker, APIs, CI/CD, monitoring and MLOps practices. Ability to understand infrastructure capacity, latency, scalability and cost implications of ML systems. Strong problem-framing ability — able to understand unfamiliar operational domains quickly and convert business behaviour into measurable data-science problems. Comfortable working directly with product managers, engineers, operations teams and senior management. Bias towards actionable intelligence over dashboards. What success looks like Operational teams proactively receiving prioritised risks and recommended actions. Reduced manual analysis and firefighting. Models monitored for accuracy, drift, latency and business impact. A reliable and cost-efficient AI/ML production architecture. Clear evidence that data science is improving delivery performance, cost, risk, revenue protection or customer experience.