Senior Data Scientist

Responsibilities
Key Responsibilities

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.

Required Skills

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