Lead Data Scientist, AdTech
Own the end-to-end data science engine for priority verticals to drive measurable revenue and media efficiency. Focus on building and deploying models for Insurance and Advertiser Quality to improve ROAS and lead quality.
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Own the end-to-end data science engine for priority verticals to drive measurable revenue and media efficiency. Focus on building and deploying models for Insurance and Advertiser Quality to improve ROAS and lead quality.
Own the end-to-end data science engine for priority verticals to drive measurable revenue and media efficiency. This involves framing business problems with stakeholders and building models to improve ROAS and lead quality.
Design and implement advanced ML models and statistical methods to optimize forecasting and decision-making for a DoD customer. Manage data provenance, lineage reporting, and resource utilization within an Agile development framework.
The role involves collecting, cleaning, and validating data from various sources to generate reports and dashboards. Additionally, the candidate will support automation workflows and AI-powered data processes using APIs from platforms like Google, Meta, and TikTok.
Build and productionize ML systems to enhance personalization, search, and recommendation experiences. Focus on utilizing advanced algorithms to improve user growth and business conversion rates.
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Analyze business needs to implement Machine Learning and Deep Learning models for data valuation. Contribute to the Data Science community through technical reviews, POCs, and internal training.
Leads the analysis of complex structured and unstructured data sets using advanced statistical methods to drive business decision-making. Responsible for managing cross-functional teams, developing custom algorithms, and providing technical mentorship to other data scientists.
Design, develop, and deploy machine learning models and predictive analytics solutions to support strategic business decisions. Collaborate with engineering and business teams to build scalable data pipelines and mentor junior data scientists.
The role involves building and deploying ML and LLM-powered features to automate governance, risk, and compliance workflows. You will manage the full lifecycle from problem framing and data exploration to production monitoring and integration.
Lead end-to-end ML project implementations for clients, translating business problems into production-grade technical solutions. Design and maintain scalable ML pipelines and Generative AI applications using MLOps best practices.
Develop a POC platform to transform unstructured contract PDFs into a searchable AI-powered dashboard. This includes building the frontend, backend services, and integrating LLMs for structured data extraction.
Lead and manage end-to-end data science projects from inception to production, ensuring alignment with strategic business objectives. Collaborate with cross-functional teams to design AI systems and communicate technical insights to stakeholders.
Lead the end-to-end delivery of advanced statistical and machine learning models for credit risk, pricing, and fraud. Partner with cross-functional teams to integrate these models into applications and mentor team members on technical standards.
Lead the end-to-end delivery of data science initiatives to develop and deploy advanced models for credit risk, pricing, and fraud. Partner with cross-functional teams to integrate these models into applications and mentor team members on technical standards.
Lead the growth strategy and experimentation culture to drive activation, engagement, and monetization. Build self-service analytics tools and collaborate with product and engineering teams to design and analyze experiments.
The Senior AI Engineer / Data Scientist will lead end-to-end ML implementations with clients, translating business problems into technical solutions. They will also design and maintain production-grade ML pipelines and deploy advanced Generative AI and NLP applications.
Drive strategic product decisions by analyzing user behavior, creator activity, and economy verticals. Design experiments and build durable analytical assets to improve product metrics and decision-making frameworks.
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The role involves analyzing diverse datasets to optimize underwriting, pricing, and cyber risk selection. You will develop statistical and machine learning models to provide data-driven insights and automate underwriting workflows.
The role involves analyzing diverse datasets to optimize underwriting, pricing, and risk selection for cyber insurance. You will develop statistical models and automate workflows while collaborating cross-functionally to improve risk assessment methodologies.
Drive the end-to-end development and deployment of machine learning capabilities for customer-facing SaaS products. Partner with product and engineering teams to integrate ML models into workflows and establish evaluation strategies to measure business impact.
Drive user growth, engagement, and revenue by owning end-to-end product analytics and designing rigorous A/B experiments. Partner with cross-functional leaders to translate complex data insights into actionable product strategy and roadmap prioritization.
Serve as the primary data science partner to the Marketing team to build a measurement foundation and KPI frameworks. Develop core models for LTV, ROAS, and CAC to influence budget allocation and growth strategies.
Drive user growth, engagement, and revenue by designing and executing ML projects and A/B experiments. Partner with product teams to translate business questions into analytical models and actionable product recommendations.
Own retention and churn by building predictive models to identify at-risk customers and power personalized interventions. Manage the full ML lifecycle from design and experimentation to production deployment and monitoring.
Develop and improve machine learning algorithms for face authentication and fraud prevention. Research and prototype presentation attack detection methods to enhance biometric security and accuracy.
Analyze medical and pharmacy claims data to predict healthcare provider prescribing behavior and develop AI-powered synthetic personas. Build scalable data pipelines and backend APIs on AWS to deliver market intelligence and forecasting insights.
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Research and develop data science methods to improve the predictive performance of the NEXUS Large Tabular Model across diverse enterprise datasets. Collaborate with R&D and Engineering teams to ship production-grade Python components and validate approaches on real customer data.
Own and evolve pricing models across a diverse portfolio of collectibles, including designing approaches for sparsely-traded assets. Build data quality systems and ship production code into pricing pipelines in collaboration with engineering and operations teams.
Lead the design and deployment of fraud prevention models and machine learning solutions across global markets. Partner with cross-functional teams to align data science capabilities with commercial objectives and enterprise governance standards.
Design and develop systems to analyze unstructured big data to generate actionable insights for client services and product enhancement. Collaborate with product teams to identify analysis questions and develop automated processes to cleanse and integrate large datasets.
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