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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Data Scientist - **Company:** Amazon.com, Inc. - **Location:** Jersey City, NJ, United States - **Experience:** Expert - **Salary:** $123,500.0 - $170,000.0 - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Data Analysis, Microsoft Azure, Big Data, Cluster Analysis, Computer Programming, Content Analysis, Decision Support Systems, Fraud Prevention and Detection, Statistical Hypothesis Testing, Information Retrieval, Python (Programming Language), Logistic Regression, Machine Learning, Regression Analysis, Natural Language Processing, NumPy, OpenAI, Tensorflow, Standard Sql, Search Technologies, Tokenization, Unstructured Data, Management of Software Versions, Pinecone, Feature Engineering, Chatbots, Pytorch, LangChain, Retrieval-Augmented Generation, Transfer Learning, Large Language Models, Random Forest, Prompt Engineering, Deep Learning, Model Validation, Llamaindex, Generative AI, Pandas, Few-Shot Learning, Scikit Learn, Real-time Inference, HuggingFace, Xgboost, Feature Selection, Machine Learning Operations, FAISS, Claude, Evaluation of Large Language Models, Model Explainability, Unsupervised Learning, ChromaDB - **Published:** October 4, 2026 - **Apply:** https://www.careerjet.com/job/us50ad003dc683aefa9562b24251540110/eaa ## About the Role We are looking for a highly experienced and hands-on Senior Data Scientist to join our Data Science and Advanced Analytics team. The ideal candidate will have a strong foundation in Machine Learning, Statistical Modeling, Predictive Analytics, and Generative AI/LLMs, with experience solving complex business problems within Financial Services and/or Insurance. The role will be responsible for designing, developing, and deploying production-grade analytical and AI solutions using structured and unstructured data. The candidate should have strong expertise in Python, statistics, supervised and unsupervised learning, feature engineering, model development and validation, NLP, LLMs, embeddings, and Retrieval-Augmented Generation (RAG). Experience applying analytics and AI techniques to areas such as underwriting, claims, risk analysis, fraud detection, customer analytics, pricing, financial forecasting, portfolio analysis, and operational analytics is highly preferred., * 7+ years of experience in Data Science, Advanced Analytics, Machine Learning, or Statistical Modeling, with hands-on experience delivering enterprise-scale solutions. * Strong expertise in statistics and applied mathematics, including probability, hypothesis testing, statistical inference, regression analysis, experimental design, distributions, sampling, and model validation. * Strong hands-on experience across traditional and advanced Machine Learning algorithms, including: * Linear and Logistic Regression * Decision Trees and Random Forest * Gradient Boosting, XGBoost, LightGBM * Clustering and segmentation * Time-Series Forecasting * Anomaly Detection * Feature Engineering and Feature Selection * Model Explainability and Interpretability * Strong programming skills in Python, including libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, and Transformers. * Strong SQL and analytical data-processing skills with the ability to analyze complex and large-scale datasets. * Hands-on knowledge of Generative AI, Large Language Models, NLP, transformers, embeddings, semantic search, prompt engineering, and RAG architectures. * Experience working with LLMs such as OpenAI models, Claude, Llama, Mistral, or equivalent foundation models. * Experience with GenAI frameworks such as LangChain, LlamaIndex, Hugging Face, or similar frameworks. * Experience with vector databases/search technologies such as FAISS, Pinecone, ChromaDB, or equivalent solutions. * Experience building and deploying scalable ML/AI solutions through APIs, batch pipelines, or real-time inference services. * Working knowledge of AWS, Azure, or GCP, along with ML/MLOps practices around model deployment, monitoring, versioning, and lifecycle management. * Strong analytical and problem-solving skills with the ability to translate statistical and model outputs into meaningful business recommendations. * Excellent communication and stakeholder-management skills. Qualifications: Bachelors in data science or related field ## Description * Design, develop, and deploy Machine Learning and Statistical Modeling solutions for complex financial and insurance business problems. * Build predictive models using techniques such as regression, classification, clustering, segmentation, ensemble methods, time-series forecasting, anomaly detection, and propensity modeling. * Perform exploratory data analysis, hypothesis testing, statistical inference, feature engineering, feature selection, model validation, and performance analysis. * Analyze large-scale structured and unstructured datasets to identify patterns, trends, risk drivers, and actionable business insights. * Develop analytics and ML solutions for underwriting, claims, fraud/risk detection, customer segmentation, pricing, retention, forecasting, and portfolio analytics. * Design and develop Generative AI and LLM-based applications, including document analysis, summarization, knowledge retrieval, intelligent search, and conversational AI. * Build RAG pipelines using embeddings, semantic search, vector databases, and enterprise knowledge sources. * Demonstrate strong understanding of LLM architectures, transformers, tokenization, embeddings, context windows, prompt engineering, model selection, fine-tuning, and LLM evaluation. * Apply techniques such as prompt engineering, few-shot learning, prompt tuning, LoRA/PEFT, and fine-tuning where appropriate. * Evaluate ML and GenAI solutions across accuracy, precision, recall, F1-score, ROC-AUC, model stability, hallucination, relevance, latency, and cost, depending on the use case. * Collaborate with business stakeholders, data engineers, ML engineers, MLOps, and product teams to translate business requirements into scalable analytical and AI solutions. * Communicate complex analytical findings and model outcomes to both technical and business stakeholders, with a clear focus on business impact and decision support. * Support productionization, monitoring, governance, and continuous improvement of ML and GenAI solutions.