Data Scientist

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
• 2 to 5 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