{"id":1736263,"url":"https://alion.io/job/exl-senior-data-scientist-2","title":"Senior Data Scientist","company":{"id":38016,"name":"EXL","domain":"exlservice.com","url":"https://alion.io/company/exl","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":81,"open_postings":65,"ghost_share":0,"stale_share":0.954,"repost_share":0,"time_to_fill_p50_days":7,"computed_at":"2026-10-10T05:45:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Jersey City, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":110000,"max_usd":214000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":960},"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Embeddings","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Anomaly Detection","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Claude","optional":true},{"name":"FAISS","optional":true},{"name":"Few-Shot Learning","optional":true},{"name":"Fine-tuning","optional":true},{"name":"GCP","optional":true},{"name":"Hallucination","optional":true},{"name":"Hugging Face","optional":true},{"name":"Interpretability","optional":true},{"name":"LangChain","optional":true},{"name":"LightGBM","optional":true},{"name":"Llama","optional":true},{"name":"LlamaIndex","optional":true},{"name":"LLM","optional":true},{"name":"LoRA","optional":true},{"name":"Mistral","optional":true},{"name":"NumPy","optional":true},{"name":"OpenAI","optional":true},{"name":"PEFT","optional":true},{"name":"Pinecone","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Semantic Search","optional":true},{"name":"Semantic Search","optional":true},{"name":"SQL","optional":true},{"name":"TensorFlow","optional":true},{"name":"Time Series Forecasting","optional":true},{"name":"Tokenization","optional":true},{"name":"Transformers","optional":true},{"name":"XGBoost","optional":true}],"status":"closed","first_seen_at":"2026-10-02T13:13:18Z","employer_posted_date":"2026-10-02","last_verified_at":"2026-10-10T16:02:47Z","board_verified":false,"closed_at":"2026-10-10T16:02:47Z","days_open":8,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":8},"description":"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.\nThe 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).\nExperience 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.\nBase Compensation Range: 120,000 - 140,000\nThe posted range is the hiring range for this role - a subset of the broader range available to employees over time - and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.\nFor more information on benefits and what we offer please visit us at https://www.exlservice.com/us-careers-and-benefits\n Key Responsibilities\nDesign, develop, and deploy Machine Learning and Statistical Modeling solutions for complex financial and insurance business problems.\nBuild predictive models using techniques such as regression, classification, clustering, segmentation, ensemble methods, time-series forecasting, anomaly detection, and propensity modeling.\nPerform exploratory data analysis, hypothesis testing, statistical inference, feature engineering, feature selection, model validation, and performance analysis.\nAnalyze large-scale structured and unstructured datasets to identify patterns, trends, risk drivers, and actionable business insights.\nDevelop analytics and ML solutions for underwriting, claims, fraud/risk detection, customer segmentation, pricing, retention, forecasting, and portfolio analytics.\nDesign and develop Generative AI and LLM-based applications, including document analysis, summarization, knowledge retrieval, intelligent search, and conversational AI.\nBuild RAG pipelines using embeddings, semantic search, vector databases, and enterprise knowledge sources.\nDemonstrate strong understanding of LLM architectures, transformers, tokenization, embeddings, context windows, prompt engineering, model selection, fine-tuning, and LLM evaluation.\nApply techniques such as prompt engineering, few-shot learning, prompt tuning, LoRA/PEFT, and fine-tuning where appropriate.\nEvaluate ML and GenAI solutions across accuracy, precision, recall, F1-score, ROC-AUC, model stability, hallucination, relevance, latency, and cost, depending on the use case.\nCollaborate with business stakeholders, data engineers, ML engineers, MLOps, and product teams to translate business requirements into scalable analytical and AI solutions.\nCommunicate complex analytical findings and model outcomes to both technical and business stakeholders, with a clear focus on business impact and decision support.\nSupport productionization, monitoring, governance, and continuous improvement of ML and GenAI solutions.\nRequired Skills\n7+ years of experience in Data Science, Advanced Analytics, Machine Learning, or Statistical Modeling, with hands-on experience delivering enterprise-scale solutions.\nStrong expertise in statistics and applied mathematics, including probability, hypothesis testing, statistical inference, regression analysis, experimental design, distributions, sampling, and model validation.\nStrong hands-on experience across traditional and advanced Machine Learning algorithms, including:Linear and Logistic Regression\nDecision Trees and Random Forest\nGradient Boosting, XGBoost, LightGBM\nClustering and segmentation\nTime-Series Forecasting\nAnomaly Detection\nFeature Engineering and Feature Selection\nModel Explainability and Interpretability\n\nStrong programming skills in Python, including libraries such as pandas, NumPy, scikit-learn, PyTorch, TensorFlow, XGBoost, and Transformers.\nStrong SQL and analytical data-processing skills with the ability to analyze complex and large-scale datasets.\nHands-on knowledge of Generative AI, Large Language Models, NLP, transformers, embeddings, semantic search, prompt engineering, and RAG architectures.\nExperience working with LLMs such as OpenAI models, Claude, Llama, Mistral, or equivalent foundation models.\nExperience with GenAI frameworks such as LangChain, LlamaIndex, Hugging Face, or similar frameworks.\nExperience with vector databases/search technologies such as FAISS, Pinecone, ChromaDB, or equivalent solutions.\nExperience building and deploying scalable ML/AI solutions through APIs, batch pipelines, or real-time inference services.\nWorking knowledge of AWS, Azure, or GCP, along with ML/MLOps practices around model deployment, monitoring, versioning, and lifecycle management.\nStrong analytical and problem-solving skills with the ability to translate statistical and model outputs into meaningful business recommendations.\nExcellent communication and stakeholder-management skills.\n Bachelors in data science or related field","description_format":"text","description_chars":5903,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":["Equity"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":[],"lifecycle":[{"event":"open","at":"2026-10-03T01:09:12Z"},{"event":"close","at":"2026-10-10T16:02:47Z"}],"visa":[{"country":"US","licensed_sponsor":true,"evidence":"H-1B filings in 12 months: 281 · green card filings: 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