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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and deploying machine learning and statistical modeling solutions for financial and insurance sectors, with a strong focus on Generative AI and large language models. Proficient in translating complex analytical findings into actionable business insights and recommendations.
Highest-signal resume keywords
Machine Learning AlgorithmsGenerative AIStatistical ModelingPython ProgrammingData Analysis
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Regression AnalysisClassificationClusteringFeature EngineeringTime-Series ForecastingAnomaly DetectionModel ValidationSQLStatistical InferenceExperimental Design
Soft Skills
Analytical SkillsProblem-SolvingCommunicationStakeholder Management
Tools & Technologies
PandasNumPyScikit-LearnPyTorchTensorFlowXGBoostTransformersAWSAzureGCP
Industry Keywords
Data ScienceAdvanced AnalyticsFinancial ServicesInsuranceMLOpsNLPVector DatabasesRAG ArchitecturesModel DeploymentBusiness Insights
Tech Stack
Tools & technologiesAWSAzureGoogle Cloud PlatformNumpyPandasPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Design, develop, and deploy machine learning and statistical modeling solutions for complex financial and insurance business problems
- Build predictive models using 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 machine learning 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
- Apply 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
- Collaborate with business stakeholders, data engineers, ML engineers, MLOps, and product teams to translate business requirements into scalable analytical and AI solutions
- Communicate analytical findings and model outcomes to technical and business stakeholders, focusing on business impact and decision support
- Support productionization, monitoring, governance, and continuous improvement of ML and GenAI solutions
Requirements
What you’ll need- Bachelor's degree in data science or related field
- 7+ years of experience in Data Science, Advanced Analytics, Machine Learning, or Statistical Modeling
- 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 with machine learning algorithms, including regression, classification, clustering, segmentation, ensemble methods, time-series forecasting, anomaly detection, feature engineering, feature selection, and model explainability/interpretability
- Strong programming skills in Python, including pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, and Transformers
- Strong SQL and analytical data-processing skills
- Hands-on knowledge of Generative AI, Large Language Models, NLP, transformers, embeddings, semantic search, prompt engineering, and RAG architectures
- Experience 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
- Knowledge of ML/MLOps practices around model deployment, monitoring, versioning, and lifecycle management
- Strong analytical and problem-solving skills
- Ability to translate statistical and model outputs into meaningful business recommendations
- Excellent communication and stakeholder-management skills
Benefits
Comp & perks- Benefits information provided via https://www.exlservice.com/us-careers-and-benefits
- Compensation may exceed the posted range for positions based in higher-cost zones such as California, New York, and New Jersey