Member of Data Staff (Data Scientist)

Perplexity is AI for people who expect more. This role brings that same standard to how our data team works, with AI at the center of everything we do. We're looking for someone who's been a great data scientist, analytics engineer, or data engineer: the kind of person who knows which metric actually matters, can design an A/B test that answers the real question, has gone deep on a data model because something didn't add up, and has decided that the highest-leverage thing they can do next is build AI systems that fundamentally change how data science gets done . You'll build AI agents and internal systems that can increasingly handle end-to-end analysis workflows: forming hypotheses, writing and running queries, interpreting results, and drafting recommendations with the right evaluation, review, and guardrails. You'll build the retrieval infrastructure and evaluation loops that let AI systems query the warehouse reliably. You'll create workflows that detect, diagnose, and help fix data issues before they become company-wide problems. You'll build the infrastructure that multiplies what a small data team can ship. You'll join a data team that's already using AI across its work. The next step is turning those individual workflows into scalable systems, shared tools, and an AI-native operating model that becomes a benchmark for how modern data teams work. What You'll Do Build AI agents that do data science - not just SQL copilots, but systems that can safely explore data, form hypotheses, run queries, interpret results, and generate actionable recommendations with clear evaluation and human review loops. Make AI systems query the warehouse reliably - build the retrieval infrastructure and evaluation loops that let agents use our semantic context and metadata accurately. Accelerate the AI-native data workflow - turn the best existing AI-assisted workflows into repeatable systems, reusable tools, and patterns the whole data team can adopt. Automate the data lifecycle -

Perplexity is AI for people who expect more. On the data team, that means building the systems that make our data reliable, understandable, and usable by both humans and AI. We're looking for an analytics engineer or data engineer who wants to build the foundation for an AI-native data organization. You'll design core data models, pipelines, semantic layers, data quality systems, governance practices, and warehouse workflows that power the entire company: helping teams make strategic decisions, operate the business, and move faster with trusted data. You'll also make sure those systems are secure, privacy-aware, and legible to AI agents, data scientists, and the rest of the company. This role is for someone who can operate at the boundary of analytics engineering, data engineering, data governance, and internal product. You care about dimensional modeling, dbt standards, cost-aware warehouse design, access controls, privacy, and the details that make data trustworthy. You also believe AI should make the data stack faster, easier to maintain, and more accessible across the company without weakening security or governance. What You'll Do Build the core data foundation - design and maintain high-quality data models, marts, and pipelines that make analysis fast, reliable, and reusable. Manage the data warehouse - help own warehouse architecture, environments, permissions, performance, cost, data lifecycle, and operational hygiene so the platform scales cleanly. Make the warehouse AI-readable - own the documentation, semantic context, metadata, lineage, and retrieval patterns that AI systems depend on to understand and query Perplexity's data correctly. Own data modeling standards - define and champion dbt patterns, dimensional modeling practices, naming conventions, tests, and review processes. Lead data governance practices - define standards for access, ownership, lineage, documentation, retention, quality, and sensitive data handling across the analytical warehouse.

RDQ426R282 Databricks is building the world's best and most secure platform for data and AI. We innovate and deploy industry-leading solutions in security, compliance, and governance. As a member of the Trust and Safety Data Science team, you will work on projects critical to ensuring the security and compliance of the Databricks Platform. Our customers depend on Databricks to keep their data safe, all while orchestrating millions of virtual machines across three clouds in dozens of regions around the globe. Our engineering teams build highly technical products that fulfill real, important needs in the world. We always push the boundaries of data and AI technology, while simultaneously operating with the security and scale that is critical to making customers successful on our platform. We serve many companies with varying security and compliance needs. To efficiently serve these markets, we need to understand how customers use our existing features. This requires data-driven analysis of all aspects of security programs at Databricks. Customers also trust Databricks with their most valuable data and we have the mission to build the most trusted data analytics and ML platform in the world. We’re looking to expand our Trust and Safety Data Science team. You will join a group of “full stack” data scientists who partner with engineering and security teams, focusing on strategic plans that make Databricks secure and safe for our customers. The team will use statistical and machine learning techniques for fraud and abuse detection on our platforms using state of the art methods . You can read more about some of our efforts in this blog post . The work in fraud and abuse detection is dynamic and essential, offering an opportunity to make a substantial impact in maintaining the security and efficiency of business operations. More information is available at https://www.databricks.com/trust . The impact you will have: You will develop and implement Machine Learning mod

About Taskrabbit: Taskrabbit is a marketplace platform that conveniently connects people with Taskers to handle everyday home to-do’s, such as furniture assembly, handyman work, moving help, and much more. At Taskrabbit, we want to transform lives one task at a time. As a company we celebrate innovation, inclusion and hard work. Our culture is collaborative, pragmatic, and fast-paced. We’re looking for talented, entrepreneurially minded and data-driven people who also have a passion for helping people do what they love. Together with IKEA, we’re creating more opportunities for people to earn a consistent, meaningful income on their own terms by building lasting relationships with clients in communities around the world. Taskrabbit is a hybrid company with employees distributed across the US and EU and a Built In — Best Places to Work (2022, 2023, 2024, 2025) continually ranked across multiple national and regional categories. Join us at Taskrabbit, where your work will be meaningful, your ideas valued, and your potential unleashed! Prior to applying please note: W e are currently unable to provide visa sponsorship for this position (including H-1B, OPT, F1, CPT or other employment-based visas). Candidates must be legally authorized to work in the United States without employer sponsorship now or in the future. This role is hybrid requiring 2 days in office at our San Francisco hub every Tuesday & Wednesday (located at 130 Sutter St, San Francisco, CA). About the Role Data Science plays a crucial role in driving impact at Taskrabbit. As a member of the team, you will help drive our business strategy forward through predictive insights. We are seeking a highly skilled and motivated Staff Data Scientist to join us, working closely with cross-functional teams from product, finance, engineering, risk and operations to provide data-driven insights and solutions that enhance our products and accelerate growth while minimizing marketplace losses.

About the Role The Data Platform team owns the end-to-end data lifecycle at Perplexity, from ingestion through processing, storage, and serving, powering product features, analytics, experimentation, AI workloads, and the company’s data lake. The team defines the architecture for batch and streaming systems, the orchestration and observability stack, and a self-serve data platform, while thoughtfully combining platforms such as Databricks and Snowflake with open-source technologies including Spark, Kafka, Flink, Airflow, Dagster, dbt, Iceberg, Delta Lake, and ClickHouse. In this senior/staff role, you will shape architecture, set standards, and drive the long-term technical direction of Perplexity’s data ecosystem. Key Responsibilities Design and operate large-scale batch and streaming data pipelines that directly power Perplexity product features, AI training and evaluation workflows, analytics, and experimentation. Build event-driven and streaming systems (Kafka, Kinesis, PubSub, or similar) for real-time ingestion, transformation, and delivery, alongside batch frameworks for backfills, aggregations, and offline computation. Lead the architecture of data orchestration using tools like Airflow or Dagster, owning scheduling, dependency management, retries, SLAs, and end-to-end observability for critical data flows. Set and enforce guarantees for data correctness, freshness, lineage, and recoverability, designing systems that handle rapid scale growth, partial failures, and evolving schemas without disrupting AI workloads or product experiences. Build self-serve data platforms that let engineers, data scientists, and analysts safely discover data, define contracts, and create and operate their own pipelines with minimal friction. Improve developer experience through better abstractions, opinionated paved paths, and standards for data modeling, testing, validation, and deployment, treating the data platform as a product used by many teams. Drive architectural decision

Perplexity serves tens of millions of users daily with reliable, high-quality answers grounded in an LLM-first search engine and specialized data sources. The Answer Quality team ensures that our prompts, tools, search systems, datasets, and models work together to create the best possible experience for our users. As our product and agent capabilities evolve, we need evaluation systems that are fast, reliable, production-faithful, and actionable. In this role, you will build and improve the technical foundations that support Answer Quality across Perplexity. This includes our shared evaluation infrastructure and the platform used to replay and analyze agent traces. You will work closely with data scientists, engineers, and product teams to identify quality problems, measure their impact, and turn evaluation findings into product improvements. Responsibilities Build shared evaluation infrastructure that helps teams run reliable evals, analyze results, and make product and model decisions Develop the platform for replaying and analyzing agent traces to reproduce production behavior and diagnose failures Build and operate scalable systems for processing, storing, and monitoring interaction, trace, and evaluation data Partner with data scientists, engineers, and product teams to turn answer-quality problems into evaluations, analyses, and product improvements Operate in a small, high-impact team where your work directly shapes how Perplexity measures and improves Answer Quality Qualifications 4+ years of software, data, or machine learning engineering experience shipping and operating production systems Strong proficiency in Python and SQL, with solid fundamentals in system design, data modeling, and distributed systems Experience building big-data systems, including distributed compute, large-scale storage, and high-volume pipelines Demonstrated ownership of ambiguous technical projects from initial design through production operation Ability to work effectively with