Top Software Data Science Jobs

Develop graph data models, optimize Neo4j and Cypher queries, build Python data parsers, validate mission data, and support near-real-time graph analysis. Translate analyst and customer requirements into graph-based solutions, collaborate with software developers, document schemas and processes, and address operational challenges through data modeling and graph technologies.

Drive data-informed decisions across Product, Sales, and Engineering by analyzing usage patterns, business performance, and trends. Build data pipelines, write SQL and Python analyses, create Looker and Data Studio dashboards, monitor opportunities and risks, and enable teams to use analytics tools effectively.

Build and productionize data pipelines and LLM-based, multimodal AI solutions for insurance use cases. Develop agentic AI applications using RAG, embeddings, prompting, custom tools, and evaluation frameworks. Apply responsible-AI practices, deploy and monitor machine learning systems, and lead client workshops to translate business needs into technical solutions and roadmap priorities.

Conduct CBRN operational impact analyses, evaluate scientific test results, develop hazard prediction and sortie-generation models, and create custom software and visualization tools. Support Air Force policy, doctrine, research, and strategic planning through technical reports, recommendations, briefings, and collaboration with defense, academic, and research communities.

Lead and build a data science and analytics organization focused on operations and finance analytics. Define and execute an applied AI/ML roadmap (including LLMs and agentic systems), hire and mentor teams, partner with stakeholders to translate problems into production AI capabilities, and collaborate with data engineering and platform teams to ensure data quality, orchestration, and evaluation.

The Data Scientist will turn messy media and audience data into business insights and decisions. Responsibilities include sourcing and cleaning data, analyzing trends and audience behavior, designing experiments, building and deploying forecasting and content-analysis models on AWS, maintaining trusted dashboards and reports, and applying LLMs and agentic coding tools. The role works autonomously with engineering, product, business, and company leadership in a fast-paced remote startup environment.

Analyze data affecting ranking, hiring efficiency, candidate experience, and revenue. Design experiments, build dashboards and data models, improve data quality, prototype matching and scoring models, and evaluate LLM-powered systems through rubrics and human-in-the-loop studies. Collaborate with engineering, product, and leadership to translate rigorous analysis into product decisions.

Develop and deploy data analytics, machine learning, and AI applications. Responsibilities include analyzing complex datasets, building models, deploying containerized applications to Kubernetes, integrating LLM-powered features, monitoring and troubleshooting AI systems, and creating Streamlit prototypes. The intern will collaborate with data science teams and stay current with AI, data science, and cloud-computing practices.

Develop a predictive supply chain risk assessment tool using internal supply metrics and external market data. Build and validate machine learning models for risk scoring, time-series forecasting, and anomaly detection. Create dashboards in Power BI or Tableau, engineer market-related features, integrate data pipelines, document model performance, and communicate actionable insights to supply chain planners and stakeholders.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.