Job Responsibilities

- Build and maintain mathematical optimization models — objective functions, constraints, and penalty calibration
- Encode and validate business rules, contract parameters, and operational constraints into model inputs
- Execute regression testing and model health checks before and after every production run
- Conduct scenario and sensitivity analysis with documented trade-offs and business recommendations
- Validate all input files pre-run — NULL checks, format errors, anomalies, and data readiness sign-off
- Monitor data pipeline health, audit logs, and ingestion failures across recurring run cycles
- Perform data quality assessments — missingness, outliers, duplicate records, and consistency checks
- Validate model outputs and savings calculations — ensuring mathematical accuracy and business defensibility
- Review compliance and performance tracking — compare actuals vs recommendations and document deviations
- Review output files for anomalies and formatting issues before stakeholder delivery
- Support recurring run preparation — scenario setup, input file review, and run orchestration
- Triage platform issues — classify severity, gather diagnostics, and escalate with clear context




- Monitor cloud pipeline health — ingestion, compute, and delivery layer checks
- Maintain run logs, SOPs, runbooks, and knowledge base in structured auditable format
- Partner with business stakeholders to explain model outputs and recommendations in plain language
- Mentor junior team members on best practices, coding standards, and documentation discipline

Required Skills

- 3–8+ years experience in Data Science / Operations Research.
- Hands-on experience with optimization solvers — CPLEX and OR-Tools.(Operation Research Tools)
- Python — modeling, data validation, automation scripting, production-quality coding
- SQL — data querying, pipeline monitoring, audit log review
- Cloud platform experience — AWS or equivalent
- Robust documentation and stakeholder communication skills

Preferred Skills

- Pipeline orchestration tools — Dataiku, Airflow, or equivalent
- Containerization — Docker and Kubernetes
- REST API development — FastAPI or equivalent
- Open-source solver experience — PuLP, OR-Tools
- Domain knowledge in logistics, supply chain, or network optimization
- Cloud certification — AWS

📌 AI/ML/Gen AI Engineer (Karnataka)
🏢 Ascendion Engineering
📍 Karnataka