Top Software Data Science Jobs

Support development of AI-powered enterprise applications by designing and deploying machine learning algorithms for industrial use cases such as fraud detection and predictive maintenance. Collaborate with data scientists, AI engineers, subject matter experts, and customers to identify, validate, and interpret data. Apply scalable machine learning, generative AI, and agentic AI techniques on the C3 Agentic AI Platform.

Lead data science and analytics initiatives supporting product, engineering, leadership, and insurance clients. Analyze claims and product data, design experiments, generate hypotheses, measure ROI, build dashboards and reproducible analytics, and communicate actionable recommendations. Mentor junior analysts, conduct client-facing analyses, collaborate with ML teams, investigate data issues, and shape product strategy through sophisticated statistical modeling and exploratory analysis.

Own enterprise data inventory and translate business needs into data models, quality rules, mappings, and acceptance criteria. Partner with data and software teams on pipelines, governance, monitoring, and lifecycle management. Prepare data for agentic workflows, including semantic layers, knowledge bases, retrieval indexes, and evaluation datasets. Develop reusable analytical models, reports, dashboards, and decision-support products while monitoring data quality and communicating limitations and business impact.

Lead technical direction for homeowners insurance pricing and risk modeling. Architect and deploy GLM and machine learning models, including profitability, retention, conversion, and customer lifetime value models. Use geospatial data to assess localized risk, partner with actuarial, product, and engineering teams on regulatory filings and production workflows, mentor data scientists, and champion experimentation, monitoring, MLOps, and emerging generative AI applications.

Lead customer success for an energy-sector client portfolio by developing adoption and success strategies, serving as a trusted advisor, and delivering analytics solutions using Spotfire. Apply machine learning, statistical modeling, optimization, AI, large language models, digital twins, and anomaly detection to customer problems. Collaborate with sales, support, product, and engineering; guide prototypes and R&D; provide demonstrations, training, thought leadership, and industry presentations. Coach colleagues and operate with limited oversight.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.

Build data filtering and curation pipelines for VLM and classifier training sets, develop model-based data-quality signals, design clinical evaluation methodology for radiology report generation, maintain clinical benchmarks, validate model-based evaluators against radiologist judgment, and implement continuous evaluation and regression testing across the research stack.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

As a Data Scientist, you will advance applied ML capabilities for healthcare, develop LLM-based chart abstraction techniques, and enhance data science best practices.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.