Lead - Data Scientist
First seen by Alion on Sep 7, 2026.
Role & Responsibilities:
- Lead the end-to-end lifecycle of data science and machine learning projects, from problem definition and data exploration through model development, validation, deployment, and monitoring.
- Translate complex business problems into data-driven, statistical, and machine learning solutions with clearly defined success metrics.
- Design and develop predictive, classification, recommendation, forecasting, optimization, NLP, or GenAI solutions based on business requirements.
- Establish and follow best practices for MLOps, model versioning, deployment, monitoring, retraining, and model governance.
- Leverage cloud platforms such as AWS, Azure, or GCP and relevant data/ML platforms.
- Evaluate emerging technologies in Generative AI, LLMs, NLP, deep learning, and agentic AI, and identify opportunities for business adoption.
- Define technical approaches, architecture, experimentation strategies, and project roadmaps for complex AI initiatives.
- Monitor production models for accuracy, data/model drift, bias, latency, reliability, and business impact, and drive continuous improvement.
- Lead technical discussions, conduct code/model reviews, and establish standards for data science quality and documentation.
- Mentor and coach data scientists, helping build technical capabilities and improving engineering and analytical practices across the team.
- Collaborate with senior business stakeholders to communicate insights, model outcomes, risks, and recommendations in a clear and actionable manner.
- Manage multiple projects and priorities while ensuring timely delivery and measurable business outcomes.
- Stay current with developments in AI/ML, GenAI, MLOps, and advanced analytics and evaluate their applicability to the organization.
Preferred Candidate Profile:
- 5 - 10 years of professional experience in Data Science, Machine Learning, Advanced Analytics, Applied AI, or a closely related field.
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, Engineering, or a related quantitative discipline.
- Strong hands-on expertise in Python and SQL.
- Strong understanding of machine learning, statistical modeling, predictive analytics, and experimental design.
- Proven experience developing and deploying machine learning models in production, rather than only building POCs.
- Experience with frameworks such as Scikit-learn, XGBoost/LightGBM, TensorFlow, or PyTorch.
- Experience working with large datasets using SQL, Pandas, PySpark, or similar technologies.
- Good understanding of cloud platforms such as AWS, Azure, or GCP.
- Exposure to MLOps, MLflow, Docker, CI/CD, model deployment, APIs, and production monitoring is highly desirable.
- Experience with Generative AI/LLMs, NLP, RAG, embeddings, vector databases, or agentic AI would be an advantage, particularly for a modern AI-focused role. Current Indian postings increasingly include these capabilities.
- Strong ownership mindset, attention to detail, and ability to work independently in a fast-paced environment.
- Candidates with experience in product companies, startups, GCCs, consulting, BFSI, e-commerce, SaaS, or other data-intensive industries would be preferred, depending on the business domain.
Skills
Data Scientist, Data Science, Data Management, Artificial Intelligence, Machine Learning