Top Hybrid Data Science Jobs

Own the analytics lifecycle from data ingestion and modeling through BI visualization and decision enablement. Build SQL/Python analyses, automate ROI reporting, define metrics, develop scalable ETL pipelines and data warehouses, and deliver ThoughtSpot dashboards. Improve data quality through validation and monitoring, collaborate with Product and Engineering, and communicate actionable insights to technical and non-technical stakeholders. Participate in customer-facing ROI presentations and prototype ML models to inform product decisions.

Supports data-driven decision-making through research, complex data analysis, forecasting, optimization, data mining, and statistical or machine learning modeling. The intern develops algorithms, evaluates business metrics, prepares reports and presentations, communicates insights to leadership, and recommends analytics applications for business strategies. The role also involves responsible AI use, evaluating AI outputs, and collaborating on research projects while gaining practical experience in advanced analytics.

Own the end-to-end data science lifecycle for workforce identity and hiring fraud detection. Analyze identity, device, behavioral, resume, and application data; develop fraud rules, machine learning models, and anomaly detection systems; and support deployment and monitoring in RiskOS. Collaborate on GenAI resume verification and explanation tools, evaluate LLM components, partner with engineering on productionization, and communicate model performance and customer impact to product, GTM, and consulting stakeholders.

Build and validate forecasting, optimization, anomaly detection, and measurement models using complex customer data. Perform exploratory analysis, experiments, backtesting, and error analysis; partner with ML and data engineers to productionize models, features, evaluation pipelines, and monitoring. Translate business questions into analytical recommendations and communicate findings to technical and non-technical stakeholders. The role is hybrid in New York City or remote in the U.S. with monthly travel to New York.

Lead end-to-end data science and AI initiatives focused on marketing optimization, predictive modeling, experimentation, and analytics. Partner with clients and cross-functional teams to translate business needs into solutions, build data pipelines, deploy machine learning and GenAI systems, and develop evaluation and safety frameworks. Manage people, project plans, stakeholder communication, and client-ready deliverables while supporting production ML and LLM applications.

Develop personalization and machine learning solutions across product, growth, and monetization. Own projects from opportunity discovery and data preparation through modeling, experimentation, product integration, launch evaluation, and iteration. Build production scoring workflows, translate predictions into product actions, apply causal reasoning, and collaborate with engineering and product teams. Projects include recommendations, promotion targeting, retention, customer lifetime value, and lifecycle management.

Leads AXS's global data organization and enterprise data, analytics, and AI strategy. Oversees data platforms, pipelines, machine learning, MLOps, business intelligence, governance, privacy, and reporting supporting personalization, pricing, fraud prevention, forecasting, and executive decision-making. Builds and scales global teams, manages roadmaps, budgets, vendors, and operational excellence, and partners with executive, product, finance, marketing, and engineering leaders to deliver measurable business impact.

Leads DAT’s Marketplace and Decision Optimization team of applied scientists. Responsibilities include hiring and developing scientists, setting technical roadmaps, building recommendation, auction, negotiation, pricing, and monetization models, and advancing experimentation and causal inference practices. The manager remains hands-on with modeling, experiments, and analysis while influencing product, engineering, finance, and go-to-market decisions across DAT’s marketplace and subscription businesses.

Develop reproducible emissions calculations and analytical methodologies using Python, SQL, and PySpark. Investigate complex energy and emissions datasets, identify data-quality issues, apply statistics and machine learning, and translate research into scalable production capabilities. Collaborate with engineering, product, research, and customer teams while contributing maintainable code through Git, testing, documentation, and code review.

Leads advanced analytics, artificial intelligence, machine learning, forecasting, optimization, and scenario modeling for electric utility transmission and distribution planning. Analyzes complex utility datasets, develops dashboards and decision-support tools, supports grid modernization and large-load integration, advises clients, communicates findings to technical and executive audiences, mentors staff, and contributes to business development and thought leadership.