Sr. Data Scientist

$125,000 - $150,000 / year + Bonus The insurance industry runs on Vertafore. We equip agencies, MGAs, and carriers with the core digital systems, specialized AI, and data-driven foundation to eliminate distribution drag across the insurance lifecycle, spanning sales, servicing, and back-office operations.  Underpinned by unmatched speed and performance power, we are the trusted backbone that’s taking the insurance industry from friction to flow with Distribution Velocity – speed, performance, and trust - to drive growth at scale.  With over 95% of the top agencies and insurers and 50% of industry compliance transactions running through Vertafore, we lead at the intersection of innovation and trust, giving insurance professionals the confidence to transform and win in the AI era.  Our reach is global, with headquarters in Denver, Colorado, and offices across the U.S., Canada, and India.  JOB DESCRIPTION At Vertafore, Data Scientists play a critical role in transforming data into intelligent products and business outcomes. This role combines advanced analytics, machine learning, generative AI, and experimentation to drive innovation across our insurance technology platform. The ideal candidate is a hands-on practitioner who can identify opportunities in complex datasets, develop production-grade machine learning and AI solutions, and collaborate cross-functionally to deliver measurable business impact. This role requires expertise in both traditional machine learning and emerging AI technologies, including large language models (LLMs), retrieval-augmented generation (RAG), agentic workflows, and AI-assisted product development.     Core Requirements and Responsibilities: Essential job functions include but are not limited to performing the following: ·       Data Discovery & Strategic Analytics o   Explore, profile, and assess large structured and unstructured datasets. o   Identify opportunities for AI, machine learning, automation, and predictive analytics. o   Partner with product, engineering, and business stakeholders to translate business challenges into data science and AI solutions. o   Source, evaluate, and integrate internal and external data assets.   ·       Machine Learning & AI Development o   Design, develop, evaluate, and deploy machine learning models that support customer, operational, and product initiatives. o   Build predictive, classification, recommendation, forecasting, and optimization models. o   Develop and evaluate generative AI solutions utilizing foundational models and LLMs. o   Implement Retrieval-Augmented Generation (RAG), semantic search, vector databases, and agent-based AI systems where appropriate. o   Fine-tune, evaluate, and monitor AI models for performance, accuracy, bias, and business value. o   Apply modern feature engineering, model selection, hyperparameter optimization, and validation techniques.   ·       AI Product Innovation o   Partner with Product Management to identify and prioritize AI-driven product capabilities. o   Develop proof-of-concept and prototypes that accelerate innovation. o   Evaluate emerging AI technologies and recommend adoption strategies. o   Contribute to AI roadmaps and Enterprise AI strategy.   ·       MLOps & Productionalization o   Build and maintain scalable ML and AI pipelines. o   Collaborate with software engineers to deploy and monitor production models. o   Implement CI/CD practices for machine learning and AI systems. o   Establish model monitoring, drift detection, retraining, observability, and governance processes. o   Ensure reproducibility, traceability, and auditability of data science assets.   ·       Responsible AI & Governance o   Apply ethical AI principles, fairness assessments, and risk management practices. o   Ensure compliance with security, privacy, regulatory, and governance requirements. o   Participate in AI governance reviews and model risk assessments.   ·       Collaboration & Technical Leadership o   Mentor data scientists and analysts o   Conduct code reviews, model reviews, and technical design reviews. o   Communicate complex technical concepts to technical and non-technical audiences. o   Contribute to best practices, standards, and reusable AI frameworks.