Lead – Data Science & AI Engineering

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.