Data Science Manager
First seen by Alion on Sep 10, 2026.
KEY RESPONSIBILITIES :
Technical Delivery & Leadership :
- Own the end-to-end delivery of multiple concurrent client projects - from problem framing and data strategy through modeling, deployment, and handover.
- Architect and review data science solutions across forecasting, optimization, recommendation, predictive maintenance, NLP/LLM, and agentic AI use cases.
- Lead, mentor, and technically guide a team of data scientists; set standards for code quality, model validation, and reproducibility.
- Ensure solutions are production-ready - scalable, well-documented, and deployable in diverse client environments.
- Manage client expectations, timelines, and deliverables, acting as the primary technical point of contact for engagements.
Presales & Solutioning :
- Partner with the business development and presales teams to scope opportunities, understand client needs, and shape tailored solution proposals.
- Contribute to proposals, solution architectures, effort estimates, and technical responses to RFPs.
- Design and present proofs-of-concept, demos, and solution walkthroughs to prospective clients.
- Translate ambiguous business problems into clear, feasible analytics/AI approaches that map to measurable client value.
Innovation & Capability Building :
- Contribute to reusable accelerators, frameworks, and IP that speed up delivery and strengthen ORMAE's solution offerings.
- Stay current with advances in ML, GenAI, agentic systems, and optimization, and bring relevant techniques into client work.
REQUIRED SKILLS & EXPERIENCE :
- Experience : 8+ years in data science / applied ML, including 2+ years leading projects or mentoring a team.
- Education : Strong quantitative background - degree in Statistics, Computer Science, Mathematics, Operations Research, or a related field.
- Core ML/Stats : Regression, classification, clustering, time-series forecasting, optimization, and statistical inference, with sound grasp of model validation.
- Modern AI : Hands-on experience with LLMs, RAG, and GenAI; exposure to agentic AI / MCP-based orchestration is a strong plus.
- Engineering : Proficient in Python and SQL; experience with cloud (Azure/AWS), containerization (Docker/Kubernetes), and deployment frameworks (Flask/FastAPI, MLflow).
- Delivery : Track record of taking models from prototype to production in real client or business environments.
- Communication : Excellent written and verbal communication; able to present technical concepts to non-technical business stakeholders.
- Presales aptitude : Comfort in client-facing settings - scoping, solutioning, and supporting business development.
GOOD TO HAVE :
- Prior experience in a consulting or client-services environment with multiple concurrent engagements.
- Domain exposure to one or more of : pharma, FMCG, healthcare, aerospace, manufacturing, e-commerce, or hospitality.
- Experience contributing to proposals, RFPs, or productized accelerators.
- Familiarity with BI/visualization (Power BI) and big-data tooling (PySpark, Databricks, Kafka).
WHAT WE OFFER :
- High-ownership role with direct exposure to diverse industries and cutting-edge AI problems.
- Opportunity to lead delivery and shape solutions from presales through production.
- A collaborative team working at the intersection of OR, ML, and GenAI.
Skills
Data Science, Data Scientist, Machine Learning, Artificial Intelligence, NLP, LLM, RAG, Python, SQL, AWS