Top Software Data Science Jobs

Analyze equipment telemetry, service history, operating conditions, and environmental data to develop predictive health scores for UPS and thermal systems. Build Python, SQL, and Tableau analytics solutions; validate incidents with field results; refine machine learning models; improve reporting and corrective-action recommendations; and collaborate with engineering, product, field, and customer teams. Apply simulation, FMEA, Six Sigma, and energy-efficiency standards to improve maintenance planning, asset reliability, and operational efficiency. Occasional travel is required.

Own product analytics across Axon’s ecosystem, partnering with product managers to define metrics, validate tracking, build durable data models, and deliver decision-focused analyses. Apply segmentation, experimentation, forecasting, and causal inference to product adoption and engagement questions. Help establish shared metric and reporting standards, maintain data quality and documentation, and explore AI agents for querying, anomaly detection, and insight generation.

Analyze blockchain and blockspace datasets to identify trends, build data pipelines, perform statistical analysis, develop models, and create dashboards. Collaborate with cross-functional stakeholders to deliver data-driven solutions, improve metrics and infrastructure, conduct research, and explore opportunities in blockchain, AI, and data science.

Develops predictive and deep learning models for engineering and physical systems, including data preprocessing, probabilistic modeling, optimization, and scalable production data pipelines. Collaborates with simulation engineers and customers to integrate AI models into simulations, support product development, and deliver practical solutions. The role includes customer presentations, onboarding, on-site collaboration, and travel to international customer sites.

Develop machine learning and advanced analytics models for sparse, noisy, and adversarial intelligence datasets. Translate analytic questions into technical requirements, assess data quality, recommend technical approaches, communicate findings to technical and non-technical stakeholders, and support automated analytics workflows for national security missions.

Develop machine learning and AI methods for cybersecurity products using large-scale network telemetry. Analyze anomalies and malicious behavior, engineer features, evaluate models on imbalanced and incomplete data, establish monitoring metrics, and conduct error analysis. Collaborate with threat researchers and software engineers to productionize solutions, research emerging techniques, communicate findings, document methodologies, and mentor data scientists.

Build Way’s data and intelligence layer across pricing, merchandising, personalization, forecasting, recommendations, and automation. Own data foundations, metrics, models, experiments, and production-ready datasets while partnering with Product and Engineering. Identify high-impact opportunities, ship measurable solutions, and establish rigorous, responsible data practices in a fast-moving startup environment.

Leads ZipRecruiter’s Marketplace Decision Science team as a player-coach, managing and developing decision scientists while personally conducting high-impact analyses. Owns marketplace quality metrics, experimentation standards, tradeoff frameworks, forecasting, and ROI modeling. Partners with Product, Engineering, Finance, and GTM leaders to shape strategy, evaluate ranking changes, quantify long-term impact, and communicate insights to executives. Requires strong product analytics, data science, experimentation, business, and people-management experience.

Evaluate and validate machine learning outputs against financial theory, client objectives, and market dynamics. Translate research signals into investment strategies, portfolio construction, and testable modeling specifications. Design and scale quantitative models in Python and Java, develop financial validation frameworks, and collaborate with ML researchers and technical teams to integrate research into production. Communicate quantitative insights clearly across technical and business stakeholders.

Analyze petabyte-scale ad and app datasets to build dashboards, monitoring, and scalable analytics pipelines. Conduct exploratory analyses, root-cause investigations, A/B test evaluation, and model performance checks. Partner with engineering, research, and business teams to define KPIs, detect anomalies, and translate insights into product and business recommendations.