We are seeking a Principal / Senior Data Scientist to lead the development of enterprise-scale AI systems across machine learning, Generative AI, and industrial analytics.

This role combines deep technical expertise, structured problem-solving, and stakeholder management to translate complex and ambiguous business needs into scalable, production-grade AI solutions that drive measurable impact.

Roles & Responsibilities :

1. Data Science & Advanced Analytics :

- Break down complex, ambiguous problems into structured analytical workstreams

- Develop and deploy models across predictive analytics, time series, NLP, and computer vision

- Apply statistical methods and ML to uncover trends, patterns, and actionable insights

- Build methodologies to evaluate predictive power of demand signals

- Use iterative modeling with cross-validation to ensure robustness and generalization

- Perform deep exploratory and descriptive analytics to influence strategic decisions

2. Data Lake & Data Engineering :

- Design and manage centralized data lakes and scalable data platforms

- Build and maintain ETL pipelines and SQL-based data systems

- Ensure data quality, reliability, and accessibility for ML use cases

- Continuously evaluate and onboard new datasets to improve model performance

- Develop deep familiarity with existing data ecosystems

3. Generative AI, RAG & Agentic Systems :

- Design and deploy LLM-powered systems (RAG pipelines, agentic workflows)

- Build LLM interfaces and copilots for business users to enable decision-making

- Fine-tune LLMs for question answering, compliance checks, and workflow automation

- Apply embeddings, prompt engineering, and retrieval strategies

- Translate complex business and regulatory requirements into intelligent AI workflows

- Stay current with advancements and experiment with emerging techniques (RAG, agentic AI, multimodal systems.

4. MLOps & Production Systems :

- Architect end-to-end ML pipelines: data training/deployment/monitoring/retraining

- Implement CI/CD, model monitoring, and automated retraining systems

- Define performance, scalability, and reliability standards

- Ensure solutions are secure, reusable, and production-ready

- Enable observability and system health tracking

5. Governance, Risk & Compliance :

- Establish Responsible AI practices (fairness, explainability, transparency)

- Ensure compliance with Indian data protection regulations (e.g., DPDP Act)

- Implement governance for model validation, auditability, and risk control

- Define standards for secure data handling and access control

6. Industrial IoT & Applied AI Systems (good to have) :

- Develop AI solutions for manufacturing, supply chain, and IoT environments

- Build systems for:

- Predictive maintenance

- Quality inspection (computer vision)

- Operational optimization

- Integrate AI outputs into real-world operational workflows

- Work with high-volume sensor and machine data

7. Stakeholder Management & Problem Translation :

- Work closely with business stakeholders to understand ambiguous requirements and translate them into structured AI/ML solutions

- Bridge the gap between business context and technical implementation

- Define problem statements, success metrics, and solution approaches collaboratively

- Drive alignment across business, product, engineering, and leadership teams

8. Business Insights & Decision Support :

- Derive and communicate clear, data-driven insights that influence business strategy

- Translate model outputs into actionable recommendations and decision frameworks

- Design and implement experimentation (A/B testing) to validate impact

- Enable stakeholders to consume insights via dashboards, AI interfaces, and reports

Experience & Skills :

- 10-12+ years in Data Science / AI with production deployment experience

- Expertise in ML, Deep Learning, NLP, Computer Vision, and LLMs

- Strong foundation in statistics and quantitative analysis

- Proficiency in R, Python, PyTorch/TensorFlow, FastAPI, Docker, MLflow

- Experience with MLOps (CI/CD, monitoring) and cloud platforms (Azure preferred, AWS)

- Strong data engineering skills (SQL, ETL, data lakes)