Data Scientist

Naveera It Consulting; United States; Hybrid; USD 120,000 - 180,000; Full time

Job summary

Naveera It Consulting is seeking a Data Scientist & Machine Learning Engineer to design and deploy end-to-end ML solutions for insurance-related use cases. You will work on large datasets, implement GenAI applications, and collaborate with product and data teams.

The role requires strong Python expertise, experience with ML libraries, and familiarity with MLOps, CI/CD, and cloud platforms. Remote-hybrid setup with global teams and production-grade models.

Qualifications

• Strong foundation in machine learning algorithms (supervised & unsupervised).
• Experience with anomaly detection, time-series, and predictive modeling.
• Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Hands‑on experience with LLMs (OpenAI, Claude, Hugging Face, etc.).
• Strong understanding of RAG (Retrieval Augmented Generation), prompt engineering & evaluation, embeddings & vector databases (e.g., FAISS, Milvus).
• Experience building GenAI applications (chatbots, document, summarization systems).
• Experience working with large datasets (batch + streaming) and data pipelines and tools (Spark, Airflow, or similar).
• Familiarity with cloud platforms (Azure, AWS, or GCP).
• Experience deploying models using APIs (FastAPI/Flask).
• Understanding of Docker, CI/CD pipelines, and model monitoring.
• Knowledge of version control and experiment tracking tools.

Responsibilities

• Design, develop, and deploy end-to-end machine learning and deep learning models for real-world business problems.
• Build scalable solutions for large-volume data processing (structured & unstructured).
• Develop and optimize Generative AI applications using LLMs (e.g., RAG pipelines, copilots, summarization, Q&A systems).
• Implement predictive analytics models such as classification, regression, clustering, and anomaly detection.
• Work on insurance-focused use cases, including:
• Claims anomaly/fraud detection
• Risk scoring and underwriting support
• Document processing (OCR + NLP pipelines)
• Build and maintain data pipelines and feature engineering workflows
• Fine-tune and evaluate LLMs and embedding models for domain-specific use cases
• Ensure model performance, scalability, and monitoring in production environments
• Collaborate with cross-functional teams (product, data engineering, business stakeholders)
• Maintain best practices in MLOps, model versioning, and CI/CD pipelines
• AI-powered claims anomaly & fraud detection systems
• Intelligent document processing and insights extraction
• Generative AI-based assistants for claims and underwriting teams
• Predictive models for risk assessment and customer insights
• Strong problem-solving and analytical thinking
• Ability to work in a fast-paced, ambiguous environment
• Effective communication with both technical and business stakeholders
• Ownership mindset with a focus on delivering production-ready solutions
• Experience with LangChain / LlamaIndex / Semantic Kernel
• Knowledge of knowledge graphs and hybrid search systems
• Prior experience in enterprise AI solution development

Skills

ML Engineer

Deep learning

Insurance

Scikit-learn

PyTorch

TensorFlow

Python

MLOps

CI/CD

XGBoost

RAG

Spark

Airflow

Azure

AWS

FastAPI

Docker

Version control

LangChain

LlamaIndex

Semantic Kernel

Enterprise AI

Tools

Scikit-learn

XGBoost

PyTorch

TensorFlow

Job description

Job Overview

Job Title: Data Scientist & Machine Learning Engineer

Experience: 4 to 15 Years

Location: Madurai, Tamil Nadu, India

Shift: 2:00 PM – 11:30 PM IST

Working Mode: Remote/Hybrid

Job Type: Full-Time

Key Skills: Machine Learning Engineer, deep learning, insurance, Scikit learn, PyTorch, TensorFlow, Python, MLOps, CI/CD, XGBoost, Retrieval Augmented Generation, Spark, Airflow, Azure, AWS, FastAPI, Docker, version control, claims, LangChain, LlamaIndex, Semantic Kernel, enterprise AI solution development

Key Responsibilities

• Design, develop, and deploy end-to-end machine learning and deep learning models for real-world business problems.
• Build scalable solutions for large-volume data processing (structured & unstructured).
• Develop and optimize Generative AI applications using LLMs (e.g., RAG pipelines, copilots, summarization, Q&A systems).
• Implement predictive analytics models such as classification, regression, clustering, and anomaly detection.
• Work on insurance-focused use cases, including:
  • Claims anomaly/fraud detection
  • Risk scoring and underwriting support
  • Document processing (OCR + NLP pipelines)
  • Build and maintain data pipelines and feature engineering workflows
  • Fine-tune and evaluate LLMs and embedding models for domain-specific use cases
  • Ensure model performance, scalability, and monitoring in production environments
  • Collaborate with cross-functional teams (product, data engineering, business stakeholders)
  • Maintain best practices in MLOps, model versioning, and CI/CD pipelines

Required Skills & Qualifications

• Strong foundation in machine learning algorithms (supervised & unsupervised).
• Experience with anomaly detection, time-series, and predictive modeling.
• Proficiency in Python and ML libraries (Scikit-learn, XGBoost, PyTorch/TensorFlow).
• Experience with data preprocessing, feature engineering, and model evaluation.
• Hands‑on experience with LLMs (OpenAI, Claude, Hugging Face, etc.).
• Strong understanding of RAG (Retrieval Augmented Generation), prompt engineering & evaluation, embeddings & vector databases (e.g., FAISS, Milvus).
• Experience building GenAI applications (chatbots, document, summarization systems).
• Experience working with large datasets (batch + streaming) and data pipelines and tools (Spark, Airflow, or similar).
• Familiarity with cloud platforms (Azure, AWS, or GCP).
• Experience deploying models using APIs (FastAPI/Flask).
• Understanding of Docker, CI/CD pipelines, and model monitoring.
• Knowledge of version control and experiment tracking tools.

Preferred Qualifications

• Experience in the Insurance domain (claims processing, fraud detection, underwriting analytics).
• Familiarity with document AI / OCR / NLP pipelines for insurance workflows.
• Experience with graph-based or network-based anomaly detection.
• Exposure to multi-agent systems or AI orchestration frameworks.
• Understanding of regulatory and compliance considerations in insurance AI.

Key Use Cases You Will Work On

• AI-powered claims anomaly & fraud detection systems.
• Intelligent document processing and insights extraction.
• Generative AI-based assistants for claims and underwriting teams.
• Predictive models for risk assessment and customer insights.

Soft Skills

• Strong problem-solving and analytical thinking.
• Ability to work in a fast-paced, ambiguous environment.
• Effective communication with both technical and business stakeholders.
• Ownership mindset with a focus on delivering production-ready solutions.

Nice to Have

• Experience with LangChain / LlamaIndex / Semantic Kernel.
• Knowledge of knowledge graphs and hybrid search systems.
• Prior experience in enterprise AI solution development.