Lead Data Scientist

About IHX

IHX is building India’s most trusted health tech infrastructure platform—powering

real-time, consent-led claims and data exchange between insurers and 30,000+

hospitals across 1,200+ cities. With $1B+ in claims processed yearly, IHX sits at the

center of the next-gen health insurance stack.

About the Role

We are building the next-generation automated health-insurance claims processing platform, leveraging AI/ML, deep learning, NLP, OCR, and LLM-powered intelligence. As a Lead Data Scientist, you will drive the design, development, deployment, and optimization of AI models that power large-scale claims decisioning across multiple regions. This is a high-impact leadership role where you will work independently, set technical direction, mentor a diverse team, and ensure reliable production performance of mission-critical models.

Key Responsibilities

Model Development, Deployment & Production Support

• Design, develop, train, and validate models used in automated health-claims processing.
• Own the end-to-end machine learning pipelines including data ingestion, feature engineering, modeling, validation, deployment, and monitoring.
• Monitor model performance, drift, SLAs, and stability in real-time production environments.
• Lead root-cause analysis, bug resolution, and continuous improvement of production models.
• Build state-of-the-art models including classical ML, deep learning, NLP, OCR, LLMs, transformers, and generative AI.
• Implement scalable model serving and continuous training strategies using modern MLOps tools.

Build Efficient, Scalable & High-Accuracy Models

• Optimize models for accuracy, latency, memory footprint, and infrastructure cost.
• Implement model compression, distillation, and quantization when required to meet SLAs.
• Ensure solutions perform reliably across heterogeneous real-world datasets and regions.

Implement End-to-End ML Pipelines

• Architect and implement automated ML pipelines covering structured, unstructured, and document based data.
• Build feature engineering, model training, validation, and retraining workflows.
• Implement CI/CD for ML, model versioning, and automated retraining strategies.
• Work closely with engineering teams to operationalize ML using MLOps best practices.

Expertise in a Wide Range of AI Techniques

• Hands-on experience with classical ML models including tree-based models, linear models, clustering, and anomaly detection.
• Experience with deep learning architectures such as CNNs, RNNs, and Transformers.
• Strong background in NLP and LLM-based solutions for extraction, summarization, classification, and claim interpretation.
• Experience building OCR pipelines for document parsing, form extraction, and image understanding.
• Experience applying generative AI for reasoning, rule extraction, and claim scenario understanding.
• Ability to evaluate and select the most appropriate technique for each problem.

Work Independently on High-Scale Business Use Cases

• Own ML modules deployed across multiple geographies, regulations, and insurance ecosystems.
• Ensure scalability and robustness for high-volume claims processing workloads.
• Collaborate with product, engineering, and operations teams to translate business requirements into ML solutions.

Strong Technical Acumen

• Deep understanding of data structures, machine learning algorithms, and modern AI architectures.
• Proficiency in Python, ML frameworks such as PyTorch and TensorFlow, and cloud platforms including AWS, GCP, or Azure.
• Familiarity with distributed systems, microservices, APIs, and containerized deployments.
• Ability to conduct architecture reviews and guide engineering teams on ML integration.
• Experience building scalable data pipelines and feature stores.
• Define data quality standards, metadata tracking, and experiment management practices.
• Lead by example with strong individual contributions on critical projects.
• Write high-quality, production-ready Python code using frameworks such as PyTorch, Hugging Face, LangChain, or Ollama.
• Conduct rigorous model validation, interpretability analysis, and bias detection.

Team Leadership, Mentoring & Collaboration

• Lead, mentor, and inspire data scientists, ML engineers, and analysts.
• Foster a culture of ownership, experimentation, innovation, and continuous learning.
• Collaborate cross-functionally with product, engineering, quality, and operations teams.
• Demonstrate empathy, flexibility, and leadership in fast-paced environments.

Required Qualifications

• Engineering degree is mandatory.
• 8+ years of experience in data science or machine learning, with 3–5 years in a leadership role.
• Proven experience building and deploying ML models in production at scale.
• Strong foundation in statistics, machine learning fundamentals, optimization, and deep learning.
• Expertise in NLP, transformers, LLM fine-tuning, embeddings, computer vision, OCR, time-series modeling, and predictive modeling.
• Advanced proficiency in Python, SQL, ML frameworks, and cloud platforms.
• Demonstrated success leading teams and delivering enterprise-scale AI systems.

Preferred Qualifications

• Experience in health-insurance or health-claims processing ecosystems.
• Understanding of regulatory and compliance constraints in healthcare data.
• Knowledge of healthcare data standards such as HL7, FHIR, ICD, CPT, and SNOMED.
• Experience with MLOps tools including MLflow, Kubeflow, Airflow, Docker, and CI/CD pipelines.