The role investigates coordinated abuse and financial risk across a prediction-market platform, develops detection rules and monitoring, and partners with product and compliance on controls. It requires 7+ years in risk or fraud analytics, expert SQL, strong investigative judgment, and experience balancing false positives against missed abuse.

Owns full-funnel growth analytics for a vertical, guiding acquisition spend, experimentation, retention, and investment priorities. The role requires 7+ years of growth, marketing, or product analytics experience, expert SQL, strong GTM metric fluency, and effective cross-functional communication.

Build and own finance reporting models, reconcile ledger and platform activity, and analyze fees, incentives, revenue, and trader retention. The role requires 7+ years of experience, expert SQL, data science methods, accounting knowledge, and cloud data warehouse experience.

Own Polymarket’s analytics modeling layer, transforming trading and application data into trusted datasets, standardized metrics, and self-serve reporting. The role requires 7+ years of experience, expert SQL, dbt or equivalent modeling experience, orchestration, cloud warehouse expertise, and strong dimensional-modeling skills.

Own product analytics for a consumer prediction-market platform, defining success metrics, designing experiments, and translating behavioral insights into product decisions. The role requires 7+ years of experience, expert SQL, strong experimentation expertise, and excellent product communication.

Build and evolve large-scale data collection infrastructure and architectures supporting user behavior data, personalization, and recommendation systems. The role requires strong Java and SQL experience, JVM-based data processing expertise, and familiarity with cloud, Kubernetes, and modern engineering practices.

Leads a company-wide shift toward agentic engineering while remaining hands-on in software development, cloud architecture, prototyping, and technical ownership. The role requires extensive full-stack and SaaS experience, strong AI/LLM expertise, and the ability to influence global engineering teams.

Build and scale distributed data ingestion, standardization, and big-data pipelines while improving platform reliability, security, and cost controls. The role requires 8+ years of data engineering experience, technical leadership, cloud deployment expertise, and strong customer-facing communication.

Leads data governance and security across the lakehouse, analytics stack, and internal data products, establishing classification, access, quality, and compliance controls. Requires 5+ years in data governance, data security, GRC, or privacy engineering and experience with modern data platforms and audits.

The role delivers data science projects spanning predictive modeling, experimentation, causal analysis, and AI-powered workflows to improve product, marketing, and partner outcomes. It requires 4–7+ years of quantitative experience, strong Python and SQL skills, and the ability to communicate insights across technical and business teams.

Leads Finance data foundations, semantic modeling, reporting, and AI-enabled analytics while managing a team of finance data professionals. The role requires substantial business intelligence or financial analytics experience, strong finance and accounting knowledge, and proficiency in SQL and Python.

Leads the strategy, architecture, governance, operations, and adoption of an enterprise AI platform. The role builds AI agents and integrations, manages model and platform lifecycle, and ensures secure, compliant, scalable, and cost-effective AI delivery.

Build and operate machine-learning systems that improve search ranking quality across retrieval and later-stage ranking. The role requires deep search or recommender-systems expertise, production ranking ownership, and at least five years of relevant industry experience.

Build and operate the data platform and pipelines that support billing, financial reporting, product analytics, and operational decisions. The role requires strong Python, SQL, data modeling, cloud data platform, orchestration, and infrastructure-as-code experience.

Builds governed Silver and Gold data models, semantic assets, and reusable transformation patterns on Databricks for business domains such as finance, RevOps, marketing, and HR. Requires 5+ years in analytics or data engineering, strong SQL and dimensional modeling expertise, and effective stakeholder collaboration.

Build and scale data architecture, governance, and ETL pipelines across Snowflake, Databricks, and cloud platforms. The role requires 3+ years of data engineering experience, strong API and pipeline expertise, and the ability to collaborate across technical and business teams.

Builds production data assets, analytics, and sales insights for Lyft Business’s B2B revenue organization. The role partners with Sales and account teams, defines trusted metrics, and communicates actionable findings to technical and executive stakeholders.

The Analytics Engineer will own OnePay’s analytical data foundation, building trusted dbt models, tests, documentation, dashboards, and semantic metrics on Databricks. The role requires at least three years of analytics engineering experience, expert SQL and dbt skills, strong data-quality practices, and hands-on use of AI coding tools.

The Senior Business Data Analyst enables data-driven decisions by defining KPIs, analyzing business performance, and building scalable dashboards and data models. The role requires 3+ years of BI or analytics experience, expert SQL skills, strong communication, and the ability to translate insights into operational impact.

Leads a horizontal Business Analytics team supporting operational and corporate functions, translating ambiguous questions into insights, KPIs, and recommendations. Requires 8+ years of quantitative experience, analytics team leadership, advanced SQL fluency, and hands-on use of AI tools.

Builds and deploys retrieval, knowledge representation, and ML platform components for enterprise generative AI systems. The role requires 5+ years of production ML/AI experience, strong Python skills, and expertise in RAG, embeddings, vector indexing, and semantic search.

Builds and deploys real-time clinical data integrations between EHR systems and a healthcare platform. The role requires 3+ years of production data pipeline experience, Python, cloud and relational database expertise, integration testing, and deployment support.

Builds and operates AI platform capabilities including RAG pipelines, semantic retrieval, agentic orchestration, and LLM integrations to power legal tech products. Requires 4+ years in distributed cloud systems, AI/ML experience, and proficiency in modern programming languages.

Build trustworthy infrastructure for production LLM agents, closed-loop evaluation, and autonomous research workflows. The role requires strong Python and distributed-systems experience, hands-on LLM post-training and inference knowledge, and experience operating agent systems at scale.

Conduct research and build evaluation systems for autonomous AI agents operating on real engineering workflows. The role combines production experimentation, statistical measurement, automated optimization, and post-training open-weight vision-language models using proprietary autonomous-driving data.

Leads company-wide measurement strategy, causal inference, experimentation governance, and strategic modeling for executive decisions. Requires 8+ years in data science or a quantitative discipline, advanced statistical expertise, and strong communication with senior stakeholders.

Build and evolve reliable analytics infrastructure, pipelines, schemas, and foundational datasets supporting quantitative research across strategies. The role requires strong Python and SQL skills, distributed data-platform experience, and ownership of observability, performance, and reproducibility.

Leads the technical direction and development of large-scale, GenAI-powered recommendation and feed-ranking systems. Requires 10+ years of industry experience in relevance-driven products, deep expertise in machine learning and recommendations, and strong organizational influence and mentoring skills.

Leads and builds a team of Applied Scientists developing production algorithmic systems for healthcare optimization, LLM applications, and member engagement. Requires 6+ years of relevant industry experience, strong technical judgment, and hands-on expertise across machine learning and optimization.

Leads end-to-end development of production algorithmic systems for healthcare, spanning machine learning, optimization, and LLM applications. The player-coach role requires 6+ years of industry experience, strong problem-solving and metrics judgment, and technical leadership of a small team.

Build and maintain machine learning infrastructure for autonomy teams, including model pipelines, observability, inference serving, and compiler platforms. The role requires a relevant degree, at least one year of experience, strong Python skills, and familiarity with C++.

Build and operate ML infrastructure for autonomy teams, including training and deployment pipelines, model observability, inference serving, and compiler platforms across hardware targets. Requires a degree, 3+ years of relevant experience, Python proficiency, and distributed-systems expertise.

Build and operate large-scale infrastructure for autonomous-driving model training, including distributed GPU systems, data pipelines, ML workflows, and reliability tooling. The role requires 3+ years of experience, strong Python and systems-language skills, Kubernetes expertise, and distributed-systems fundamentals.

Build and operate the infrastructure powering large-scale machine-learning training for autonomous-driving systems. The role requires Python proficiency, Kubernetes production experience, distributed-systems expertise, and ownership of reliability, observability, and operational maturity.

Leads architecture and delivery of complex finance data pipelines, AI use cases, and internal applications while partnering with Accounting, FP&A, and Finance leadership. Requires 12+ years of data engineering or finance systems experience, strong SQL/Python and Databricks expertise, and deep finance-domain knowledge.

Builds and maintains finance data pipelines, applications, dashboards, and AI-assisted tools on the Databricks platform. The role requires 7+ years of experience across finance systems, data engineering, analytics engineering, or finance and accounting, plus strong SQL and Python skills.

Develop and deploy scalable machine learning and AI systems for user-facing products, including forecasting and AutoML capabilities. The role requires strong production ML engineering, modeling, software engineering, statistics, and systems knowledge.

Leads the development of machine-learning search relevance systems, including query understanding, ranking, retrieval, and evaluation at scale. Requires 10+ years of search relevance experience and expertise in ML, NLP, or related discovery technologies.

Leads search quality for AI-agent retrieval and human search experiences, optimizing hybrid retrieval, ranking, relevance metrics, and evaluation frameworks. The role requires expertise in information retrieval, embeddings, Elasticsearch or Lucene, and RAG.

Design and develop next-generation database query and storage systems, covering optimization, distributed execution, transactions, and physical data organization. The role requires 5+ years of related systems experience and values expertise in databases, distributed systems, or performance optimization.

Owns search quality across hybrid retrieval systems serving AI agents and human users. The role focuses on ranking, relevance evaluation, embeddings, and grounding RAG outputs across heterogeneous enterprise data.

Leads the development of machine-learning search relevance systems, including query understanding, ranking, retrieval, and evaluation pipelines. The role requires 10+ years of search relevance experience and expertise in NLP, LLMs, or related discovery technologies.

Leads the design and operation of Databricks’ large-scale Data Intelligence Platform, including metrics stores, ETL frameworks, multi-cloud pipelines, governance, and infrastructure tooling. Requires extensive industry experience, distributed-systems expertise, and technical leadership across complex data infrastructure initiatives.

The Senior ML and AI Technical Solutions Engineer troubleshoots and optimizes production data, machine learning, and generative AI workloads on Databricks. The role requires 8+ years of production experience with ML/AI systems, distributed computing, cloud platforms, and programming in Python, Scala, and Java.

Build and operate Databricks’ company-wide Data Intelligence Platform, including metrics stores, ETL frameworks, orchestration, governance, and reliable multi-cloud data pipelines. The role requires 6+ years of industry experience and technical leadership on large-scale data infrastructure projects.

Leads the design, operation, and evolution of Databricks’ cross-company Data Intelligence Platform, including large-scale data systems, pipelines, governance, and infrastructure. Requires 10+ years of distributed-systems experience and substantial technical leadership on production data platforms.

Data Scientist builds and deploys ML models to combat fraud, collaborates with clients on risk solutions, and scales models to production. Requires 5+ years in data science, Python/SQL/Spark proficiency, and strong communication skills.

Research and evaluate frontier AI capabilities for cybersecurity, rapidly prototyping tools, designing rigorous benchmarks, and helping operationalize reliable capabilities into products. Requires deep security expertise, strong technical communication, and at least seven years of relevant experience.

Builds and owns production multi-agent AI infrastructure, backend integrations, and workflow automation for marketing operations. Requires 8+ years of software engineering experience, strong Python and JavaScript/Node.js skills, production LLM experience, and deep Google Cloud expertise.

Build and improve production AI systems for clinical products, owning evaluations, model behavior, agentic workflows, data flywheels, deployment, and observability. The role requires 5+ years of production ML or applied AI experience, strong Python and modern ML framework skills, and hands-on debugging expertise.