Data Loss Prevention Lead Jobs

RDA & Metrology Data Platform Engineer — Taoyuan - Fab 11, Taiwan. Apply via Workday.

Product Engineer (Advance Data Analytics), Heterogeneous Integration Group (HIG), High Bandwidth Memory (HBM) Product Engineering — Fab 10A, Singapore. Apply via Workday.

Staff Field Applications Engineer-Data Center DRAM — San Jose, CA. Apply via Workday.

Principal AI Data Strategist, Global Procurement — Fab 10A, Singapore. Apply via Workday.

Senior Full Stack Developer (Data & Analytics) — PA - West Chester, 1354 Boot Rd. Apply via Workday.

Supply Chain Data Scientist 3 — India - Chennai, Comcast India Engineering Cent. Apply via Workday.

About Anyscale: At Anyscale , we're on a mission to democratize distributed computing and make it accessible to software developers of all skill levels. We’re commercializing Ray , a popular open-source project that's creating an ecosystem of libraries for scalable machine learning. Companies like OpenAI , Uber , Spotify , Instacart , Cruise , and many more, have Ray in their tech stacks to accelerate the progress of AI applications out into the real world. With Anyscale, we’re building the best place to run Ray, so that any developer or data scientist can scale an ML application from their laptop to the cluster without needing to be a distributed systems expert. Proud to be backed by Andreessen Horowitz, NEA, and Addition with $250+ million raised to date. About Ray Data Team: Ray Data is Python-native data processing engine that is a one stop shop for all AI data processing needs. Ray Data provides performant, first-class integration with cutting edge AI frameworks using both multi-modal and structured data. The Ray Data team currently develops and maintains Ray Data . We are a team of engineers passionate about building a Data processing engine which is a one-stop shop for all of your ML/AI needs. We are looking for exceptional engineers to build, optimize, and scale Ray for modern and increasingly complex AI workloads. As part of this role, you will: Improve the performance of Ray Data and multi-modal batch inference use cases. Ensure efficient scaling across different stages of the Data pipeline in a heterogeneous environment. Building data loading solutions for production training workloads. Focus on stability and fault tolerance at high scale Working with customers and new age AI native companies in scaling their AI workloads. We'd love to hear from you if have: At least 3-4 years of relevant work experience Solid background in building scalable and fault-tolerant distributed systems Experience with data processing, database internals. Passionate about large

Synthesia is the world’s leading AI video platform for business, used by over 90% of the Fortune 100. Founded in 2017, the company is headquartered in London, with offices and teams across Europe and the US. As AI continues to shape the way we live and work, Synthesia develops products to enhance visual communication and enterprise skill development, helping people work better and stay at the center of successful organizations. Following our recent Series E funding round, where we raised $200 million, our valuation stands at $4 billion. Our total funding exceeds $530 million from premier investors including Accel, NVentures (Nvidia's VC arm), Kleiner Perkins, GV, and Evantic Capital, alongside the founders and operators of Stripe, Datadog, Miro, and Webflow. About the role The Data team manages the complete lifecycle of data for researchers - from sourcing and large-scale processing to delivering datasets that power our models. Data sits at the heart of our Research efforts and enables all other teams. As part of the Data team, you’ll work with over a million hours of video and audio data. This role exists at the intersection of applied research, data engineering, and ML infrastructure rather than being a traditional research position . You’ll build the world’s best human-centric data lake by collaborating closely with our model training teams. By understanding their requirements, you’ll extract new features and annotations that elevate our datasets. You should be passionate about enhancing model performance through high-quality, accurate datasets. Our infrastructure and pipelines are in great shape, and this role provides room to not only enhance them but also influence the team’s longer-term strategy. What we're looking for: A strong background in data-centric, applied Machine Learning, with hands-on experience improving model performance through data quality, curation, labeling, and evaluation rather than model architecture alone Experience working on the data la

Lead Marketing Data Quality Engineer — 4 Locations. Apply via Workday.

Paid Media Data Solution Architect Associate — Irving Texas United States. Apply via Workday.

Machine Learning Data Engineer (AIM2) — United States - Massachusetts - Cambridge. Apply via Workday.

Senior Analyst Account Management (Data Analysis) — Sao Paulo, Brazil. Apply via Workday.

About Us What if your work could drive change in a globally established industry, shaping processes that touch every corner of the world? At Forto, we are at the forefront of change, harnessing the power of AI to revolutionise logistics. We want to reinvent digital supply chains to be transparent, frictionless and sustainable. From day one, our mission has been to simplify global trade – creating a seamless and efficient logistics process. Your role & Mission As Data Director here at Forto, you will be responsible for creating a unified data organisation consisting of Data Science, Data Analytics and Data Engineering. You will dramatically raise the profile of data across the business, partnering with stakeholders in a number of domains to assess and support their data needs. You will own and implement a company wide data strategy, prioritizing competing demands into one cohesive and well understood approach. What you will do Be responsible for the teams (each with their own manager/technical lead) that own the 3 “pillars” of data at Forto: Data Engineering, Data Analytics and Data Science, bringing them into a unified org whilst also encouraging and respecting their specialised skills Ensure that the Data org operates a Centre of Excellence, supporting and educating the “customers” of data across the business. Break down siloes and form strong working relationships with stakeholders. Define and shape a data strategy fit for purpose across our analytical and agentic business needs. Own data governance and quality. Establish the policies, standards, ownership model, and forums that make data trustworthy. Manage day-to-day compliance with GDPR and emerging AI regulation, and set practical guardrails for data and AI use. Sponsor the data platform. Work with engineering and architecture to maintain a reliable, performant, cost-aware platform across ingestion, storage, transformation, BI, and ML. Make build/buy/partner recommendations and manage key vendor relationsh

About the Role & Team We’re looking for an Engineering Manager to lead the Data Infrastructure team within Statsig Experiment at Amplitude. You will lead a multidisciplinary team of software engineers, data engineers, and data scientists responsible for the systems that power experimentation at scale. The team owns three critical areas: Data ingestion: Collecting and importing experiment exposures, custom events, OpenTelemetry data, and real user monitoring data across SDKs, streaming systems, cloud storage, and customer data warehouses. Data computation: Building distributed computation systems that transform raw data into accurate, timely experiment results. Stats engine: Developing and productionizing the statistical methods that help customers make trustworthy decisions from their experiments. This is not a traditional data engineering management role. We are looking for a leader with a solid data science and statistical foundation who can connect advances in experimentation methodology with scalable production systems. You will help set our technical and scientific direction, translating new statistical methods and machine learning research into capabilities that customers can use reliably at scale. You’ll partner closely with data scientists, engineers, product managers, and customers to advance the state of experimentation. The ideal candidate is equally comfortable discussing causal inference and statistical power with data scientists, distributed computation architectures with engineers, and experimentation strategy with customers. What You’ll Do Lead and grow the team responsible for Statsig’s data ingestion, experiment computation, and stats engine. Define the technical and scientific strategy for advancing experimentation across both Statsig Cloud and warehouse-native deployments. Partner with data scientists and engineers to turn new statistical and causal inference methods into scalable, reliable product capabilities. Evolve our data and computatio

We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, SoFi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam. Making data driven decisions is key to Plaid's culture. To support that, we need to scale our data systems while maintaining correct and complete data. We provide tooling and guidance to teams across engineering, product, and business and help them explore our data quickly and safely to get the data insights they need, which ultimately helps Plaid serve our customers more effectively. Engineers on Data Infrastructure are domain experts in Data Warehouse, Data Lakehouse, Spark, Workflow Orchestration, and Streaming technologies. We scale our existing data pipelines in a performant and cost efficient way while creating the necessary abstractions to make developing on top of this platform extremely simple for other engineers at Plaid. Responsibilities Contribute towards the long-term technical roadmap for data-driven and machine learning iteration at Plaid Leading key data infrastructure projects such as improving ML development golden paths, implementing offline streaming solutions for data freshness, building net new ETL pipeline infrastructure, and evolving data warehouse or data lakehouse capabilities. Working with stakeholders in other teams and functions to define technical roadmaps for key backe