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
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Design, develop, train, and validate models used in automated health-claims processing.
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Own the end-to-end machine learning pipelines including data ingestion, feature engineering, modeling, validation, deployment, and monitoring.
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Monitor model performance, drift, SLAs, and stability in real-time production environments.
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Lead root-cause analysis, bug resolution, and continuous improvement of production models.
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Build state-of-the-art models including classical ML, deep learning, NLP, OCR, LLMs, transformers, and generative AI.
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Implement scalable model serving and continuous training strategies using modern MLOps tools.
Build Efficient, Scalable & High-Accuracy Models
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Optimize models for accuracy, latency, memory footprint, and infrastructure cost.
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Implement model compression, distillation, and quantization when required to meet SLAs.
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Ensure solutions perform reliably across heterogeneous real-world datasets and regions.
Implement End-to-End ML Pipelines
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Architect and implement automated ML pipelines covering structured, unstructured, and document based data.
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Build feature engineering, model training, validation, and retraining workflows.
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Implement CI/CD for ML, model versioning, and automated retraining strategies.
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Work closely with engineering teams to operationalize ML using MLOps best practices.
Expertise in a Wide Range of AI Techniques
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Hands-on experience with classical ML models including tree-based models, linear models, clustering, and anomaly detection.
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Experience with deep learning architectures such as CNNs, RNNs, and Transformers.
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Strong background in NLP and LLM-based solutions for extraction, summarization, classification, and claim interpretation.
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Experience building OCR pipelines for document parsing, form extraction, and image understanding.
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Experience applying generative AI for reasoning, rule extraction, and claim scenario understanding.
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Ability to evaluate and select the most appropriate technique for each problem.
Work Independently on High-Scale Business Use Cases
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Own ML modules deployed across multiple geographies, regulations, and insurance ecosystems.
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Ensure scalability and robustness for high-volume claims processing workloads.
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Collaborate with product, engineering, and operations teams to translate business requirements into ML solutions.
Strong Technical Acumen
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Deep understanding of data structures, machine learning algorithms, and modern AI architectures.
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Proficiency in Python, ML frameworks such as PyTorch and TensorFlow, and cloud platforms including AWS, GCP, or Azure.
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Familiarity with distributed systems, microservices, APIs, and containerized deployments.
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Ability to conduct architecture reviews and guide engineering teams on ML integration.
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Experience building scalable data pipelines and feature stores.
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Define data quality standards, metadata tracking, and experiment management practices.
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Lead by example with strong individual contributions on critical projects.
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Write high-quality, production-ready Python code using frameworks such as PyTorch, Hugging Face, LangChain, or Ollama.
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Conduct rigorous model validation, interpretability analysis, and bias detection.
Team Leadership, Mentoring & Collaboration
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Lead, mentor, and inspire data scientists, ML engineers, and analysts.
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Foster a culture of ownership, experimentation, innovation, and continuous learning.
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Collaborate cross-functionally with product, engineering, quality, and operations teams.
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Demonstrate empathy, flexibility, and leadership in fast-paced environments.
Required Qualifications
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Engineering degree is mandatory.
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8+ years of experience in data science or machine learning, with 3–5 years in a leadership role.
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Proven experience building and deploying ML models in production at scale.
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Strong foundation in statistics, machine learning fundamentals, optimization, and deep learning.
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Expertise in NLP, transformers, LLM fine-tuning, embeddings, computer vision, OCR, time-series modeling, and predictive modeling.
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Advanced proficiency in Python, SQL, ML frameworks, and cloud platforms.
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Demonstrated success leading teams and delivering enterprise-scale AI systems.
Preferred Qualifications
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Experience in health-insurance or health-claims processing ecosystems.
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Understanding of regulatory and compliance constraints in healthcare data.
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Knowledge of healthcare data standards such as HL7, FHIR, ICD, CPT, and SNOMED.
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Experience with MLOps tools including MLflow, Kubeflow, Airflow, Docker, and CI/CD pipelines.
