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 Data Scientist, you will contribute to the design, development, deployment, and optimization of AI models that power large-scale claims decisioning across multiple regions. This is a hands-on technical role where you will work alongside senior data scientists, gain exposure to end-to-end ML pipelines, and grow your expertise in a fast-paced, mission-critical environment.

Key Responsibilities
Model Development, Deployment & Production Support

Contribute to the development, training, and validation of ML models used in automated health-claims processing.
Assist in building and maintaining end-to-end machine learning pipelines including data ingestion, feature engineering, and model evaluation.
Experiment with classical ML models (tree-based, linear, clustering) and modern approaches (NLP, transformers) under senior guidance.
Support monitoring of model performance and data quality in production environments.
Implement and test model improvements based on feedback and performance metrics.

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

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.

Experience in 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.
Ability to evaluate and select the most appropriate technique for each problem.

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.
Experience building scalable data pipelines and feature stores.
Define data quality standards, metadata tracking, and experiment management practices.
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.

Collaboration & Communication

Collaborate with senior data scientists, ML engineers, and product teams to deliver solutions
Document experiments, findings, and model decisions clearly
Participate in code reviews, technical discussions, and knowledge-sharing sessions
Proactively communicate progress, blockers, and learnings to the team

Required Qualifications

Engineering degree in Computer Science, Statistics, Mathematics, or a related field
1-2 years of experience in data science or ML (including internships or academic projects)
Solid understanding of ML fundamentals: supervised/unsupervised learning, model evaluation, overfitting
Hands-on experience with Python and key libraries: scikit-learn, pandas, NumPy
Hand-on experience with LLMs, RAG and Agentic Pipelines
Familiarity with at least one deep learning framework: PyTorch or TensorFlow
Basic knowledge of NLP: tokenization, embeddings, text classification
Working knowledge of SQL for data querying and manipulation
Exposure to cloud platforms (AWS, GCP, or Azure) and version control (Git)
Strong analytical thinking and problem-solving skills
Eagerness to learn, take ownership, and grow in a fast-paced environment

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