Data Loss Prevention Lead Jobs

Senior Data Scientist Description - The Team We are an expanding team at HP that develops applications that make use of Generative AI and Large Language Models. We work with business units mainly from the Commercial Organization to develop and run solutions that help our sales teams and customers. Responsibilities Defines and implements AI solutions to create business value and innovation. Works with Data Science leaders to develop new innovative solutions to existing or new business challenges. Develops clear presentations for business stakeholders and managers. Manages relationships with business partners to evaluate and foster data driven innovation, provide domain-specific expertise in cross-organization projects/initiatives. Knowledge & Skills Proficiency in Python and PySpark Programming: Strong coding skills in Python for data manipulation, model development, and integration with Azure services. Experience with Azure Services: Knowledge of key Azure services like Azure Machine Learning, Azure AI Search, and Azure Functions for deploying RAG systems. Expertise in Databricks: Ability to design, develop, and optimize workflows in Azure Databricks for data processing and feature engineering. Understanding of NLP and GenAI concepts: Familiarity with Large Language Models, prompt engineering for LLMs, vector databases, and Retrieval Augmented Generation (RAG) systems. Experience in Applied Statistics and Algorithms: Use statistics, mathematics, algorithms, and programming to address business challenges. Familiarity with the deployment and scaling RAG systems in production, using Azure’s containerization options such as Docker, AKS (Azure Kubernetes Service), or Azure Funct

Senior Data Scientist Description - Job Summary • This role is responsible for enabling innovation and creativity by bringing cutting edge perspectives on adopting latest data mining and modelling techniques. The role understands current complex business problems and future business strategy to assess, build and deploy required data mining and modelling capabilities. The role is involved in driving standardization, productivity and cross team learning by establishing processes and SOPs for entire data model development lifecycle. The role drives excellence through continuous improvement in model accuracy and reliability. Responsibilities • Leads organization wide team or teams of other data science professionals in complex projects to mine data using modern tools and programming languages. • Defines models to uncover patterns and predictions creating business value and innovation. • Manages and creates relationships with business partners to evaluate and foster data driven innovation, provides domain-specific expertise in cross-organization projects/initiatives. • Ties insights into effective visualizations communicating business value and innovation potential. • Works with various stakeholders, including business leaders, engineers, product managers, and data analysts, to identify business problems and develop data-driven solutions. • Prepares and presents literature, presentations, invention disclosures for peer review & publication in industry data science domain initiatives and conferences. • Assures insights are communicated regularly and effectively, reviewing designs, models and data compliance. • Defines, communicates and drives data insights/innovation into the business. • Leverages recognized domain expertise, business acumen, and overall data systems leadership to influence decisions of executive business

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: Position Overview The Principal Data Engineer (IC) is a senior individual contributor and the accountable technical leader for assigned cross-domain initiatives and enterprise data engineering capabilities. The role owns integrated technical direction and technical outcomes for work spanning multiple data domains, defines and stewards enterprise engineering standards and reference architectures, and drives convergence where duplicated or inconsistent solutions create enterprise cost, risk, or operational burden. The role advises on scope, sequencing, capacity, dependencies, and technical debt, but does not independently commit domain resources or business delivery dates. This position has no people-management responsibility. Enterprise Data operates a domain-aligned model built on Databricks and Unity Catalog. Working with Domain Leaders, Staff Engineers, Platform Engineering, and partner organizations, the role converts ambiguous enterprise needs into executable architecture and carries the most complex or highest-risk work through validation and production. The role remains hands-on through prototyping, reference implementations, critical-path development, design and code review, and production problem solving. This role is based in Madison, WI. Essential Duties Include, but are not limited to, the following: Cross-domain technical leadership and delivery Own the technical outcome of assigned cross-domain initiatives from initial ambigu

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare, with leading businesses and products in diagnostics, medical devices, nutritionals and branded generic medicines. Our 122,000 colleagues serve people in more than 160 countries. JOB DESCRIPTION: The Staff Data Engineer is a senior, hands-on technical contributor within an Enterprise Data domain, owning technical design and the most complex implementation work for assigned data products, capabilities, and integrations. The role establishes, communicates, and evolves the technical approach for the work it leads and is accountable for the quality, durability, and supportability of the solutions it shapes. Staff Data Engineers work directly with business stakeholders to understand needs and shape technical solutions, engaging at a level appropriate to the work they lead. The role is expected to be fluent in both technical execution and business context, translating between them without losing precision in either. The Staff Data Engineer sets technical direction for assigned capabilities and initiatives within the domain, applies enterprise standards and platform patterns, engages Principal Engineers where cross-domain considerations apply, and multiplies the effectiveness of the domain team through design leadership, code review, and mentoring. This role is based in Madison, WI . Essential Duties Include, but are not limited to, the following: Technical design and solutioning Own technical design and hands-on delivery for the most complex or highest-risk work across assigned data products, capabilities, and initiatives, including data models, pipeline architecture, integration patterns, and platform usage decisions. Produce design docume

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Data Product Experiences (DPE) team is dedicated to making Stripe's merchant-facing data products intuitive, delightful, and actionable. We build client-side rendering engines, high-performance charting libraries, and interactive visualization interfaces that handle high-throughput streaming telemetry and real-time merchant analytics. What you’ll do In this role, you will architect and build high-performance client-side data rendering engines from scratch to visualize tens of thousands of data points smoothly. You will collaborate closely with Designers, Product Managers, and Front-End Systems Engineers to define cutting-edge data visualization paradigms that reduce cognitive load and prevent visual fatigue for Stripe's users. Responsibilities Partner with designers and product managers to prototype, build, and ship interactive analytics dashboards and data visualization tools across the Stripe Dashboard. Contribute to Stripe’s core design system by developing scalable, reusable visualization components and charting standards for engineering teams. Design frontend integration strategies and robust API contracts to enable seamless adoption of analytics components by partner teams. Provide technical leadership and mentorship through code reviews, pairing, and fostering frontend engineering best practices. Collaborate across Product, Design, Data Infrastructure, and Analytics teams to deliver high-quality, data-driven user experiences. Opti

Who we are About Stripe Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career. About the team The Data Product Experiences (DPE) team is dedicated to making Stripe's merchant-facing data products intuitive, delightful, and actionable. We build client-side rendering engines, high-performance charting libraries, and interactive visualization interfaces that handle high-throughput streaming telemetry and real-time merchant analytics. What you’ll do In this role, you will architect and build high-performance client-side data rendering engines from scratch to visualize tens of thousands of data points smoothly. You will collaborate closely with Designers, Product Managers, and Front-End Systems Engineers to define cutting-edge data visualization paradigms that reduce cognitive load and prevent visual fatigue for Stripe's users. Responsibilities Partner with designers and product managers to prototype, build, and ship interactive analytics dashboards and data visualization tools across the Stripe Dashboard. Contribute to Stripe’s core design system by developing scalable, reusable visualization components and charting standards for engineering teams. Design frontend integration strategies and robust API contracts to enable seamless adoption of analytics components by partner teams. Provide technical leadership and mentorship through code reviews, pairing, and fostering frontend engineering best practices. Collaborate across Product, Design, Data Infrastructure, and Analytics teams to deliver high-quality, data-driven user experiences. Opti

We’re building a world of health around every individual — shaping a more connected, convenient and compassionate health experience. At CVS Health®, you’ll be surrounded by passionate colleagues who care deeply, innovate with purpose, hold ourselves accountable and prioritize safety and quality in everything we do. Join us and be part of something bigger – helping to simplify health care one person, one family and one community at a time. POSITION SUMMARY CVS Health is seeking a highly skilled Senior Data Engineer, Observability Engineering to join the Enterprise Observability Platform organization and help advance the next generation of observability, infrastructure, and security data capabilities. The Senior Data Engineer, Observability Engineering will play a critical role in designing, building, and operating scalable data pipelines and data products that power enterprise observability, operational intelligence, and security analytics across the organization. The Senior Data Engineer, Observability Engineering is a senior individual contributor responsible for developing and optimizing Databricks-based data engineering solutions that ingest, transform, govern, and deliver high-volume telemetry, infrastructure, application, and security data. This role combines deep hands-on technical execution with ownership of engineering excellence, operational reliability, performance optimization, and data platform best practices. Working closely with Observability Engineering, Security Engineering, Infrastructure Engineering, and Data Platform teams, the Senior Data Engineer, Observability Engineering will contribute to the evolution of the enterprise observability lakehouse by building resilient ingestion frameworks, establishing data quality standards, enhancing governance controls, and driving efficient, scalable data processing patter

Use Your Power for Purpose Global Commercial Analytics (GCA) harnesses the power of data to drive robust analytical insights that inform some of Pfizer's most critical business questions. With colleagues across the globe, GCA's rigorous analytical expertise is depended on as the compass and decision support for the enterprise. Our dynamic, exciting team of subject-matter experts comes from diverse backgrounds and experiences, including data science, market research, digital analytics, finance, and consulting. As a team, we partner to turn data into meaningful insights that will have a direct impact on patients' lives and the future of Pfizer as a data-driven organization. The Data Science Manager is accountable for delivering data science support across the commercial business. As a strategic partner to US Commercial teams, this person will develop and implement models and data science-derived insights that influence brands’ strategic priorities. These responsibilities will include driving the execution and interpretation AI/ML models, framing problems, and shaping solutions. This role is dynamic, fast-paced, highly collaborative, and covers a broad range of strategic topics that are critical to our business. The successful candidate will join GCA colleagues worldwide that are constantly supporting business transformation through their proactive thought leadership, innovative analytical capabilities, and their ability to communicate highly complex and dynamic information in new and creative ways. What You Will Achieve Commercial Data Science and Insights Provide data science and insights to US Commercial teams to drive brand tactic decisions Assist to frame, investigate, and translate complex data-informed models, and answer key business questions related to the identification and evaluation of brand strategies a

NVIDIA’s DGX Cloud organization is seeking a Senior Data Engineer to become part of its data team! We develop the reliable data foundation that supports fleet health, capacity, utilization, cost, reliability, and operational decision-making throughout DGX Cloud. Our platform supports engineering, operations, finance, and product teams managing and expanding large GPU fleets across cloud service providers and NVIDIA Cloud Partners. We are looking for a practical engineer and technical lead to take charge of a key part of the Navigator data platform. We develop the systems that transform distributed infrastructure telemetry and operational data into dependable, managed data products that support fleet health, capacity, utilization, cost, and operational decisions. We are seeking a hands-on, platform-minded engineer to build and evolve the systems that turn distributed infrastructure telemetry and operational data into reliable, governed data products. You will work across ingestion, transformation, data quality, platform architecture, security, observability, and self-service consumption to help make Navigator and the DGXC data platform a dependable source of truth. We do expect strong engineering fundamentals, experience operating production systems, and the ability to learn new platforms and domains quickly. What you'll be doing: Own systems end to end. For example, work from ambiguous customer and operational needs through architecture, implementation, deployment, observability, incident response, and ongoing support. Construct data pipelines and products. Such as designing and maintain batch and streaming ingestion, transformation, reconciliation, and serving paths for fleet, capacity, utilization, cost, scheduling, and operational telemetry. Build shared libraries, workflow and DAG or equivalent experience abstractions to evolve the data platform. Develop deployment tooling, data

Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential. Title and Summary Senior Data Scientist-2 Overview Product Data & Analytics is a centralized global team that enables Mastercard product business units to make better data‑driven decisions. We build internal analytics partnerships that strengthen focus on business performance, portfolio and revenue optimization, initiative tracking, new product development, and go‑to‑market strategies. These capabilities are underpinned by our data platforms that enable insights to be delivered in a standardized, scalable, and cost‑efficient manner. Key Responsibilities Strategic Support • Design and implement value enablement frameworks that optimize pricing strategies, enhance pre-sales propositions, and ensure customer success. • Collaborate with global and regional teams to tailor solutions that meet regional business needs and align with Mastercard's objectives. • Provide data-driven insights and recommendations to optimize pricing, pre-sales strategies, and customer success outcomes. • Develop frameworks, project structures, and presentations to communicate key strategic initiatives. • Conduct data integrity checks and ensure quality and reliability in data used for analysis. • Translate complex business problems into analytical solutions that support strategic decision-making. Technical Leadersh

About the Role: We're hiring Senior and Staff Data Platform Engineers to join the Data Infrastructure teams in Toronto. Together these teams own the infrastructure that processes billions of events per day: Spark-on-Kubernetes, Flink and Kinesis pipelines, a multi-petabyte Delta Lake, a large-scale MemoryDB feature store, Databricks multi-environment operations, and the catalog and lifecycle systems that govern it. The team is small and senior. Each engineer owns major platform components: you design it, build it, and support it in production. This is a hybrid-role based out of our Toronto office. You must be willing to travel to our Toronto office two days/week. What You'll Do: Spark-on-Kubernetes — EKS-based compute platform for Spark workloads: cluster configuration, Pod Identity IAM, job environment setup, Kustomize overlays, and shadow canary validation Event ingestion — Rust services and Flink jobs processing billions of events per day over Kinesis; throughput, reliability, on-call response, and AI-assisted operational tooling to reduce toil Platform infrastructure — Terraform modules for environment provisioning, cross-account AWS IAM, ARC runner infrastructure, and CI/CD for data platform changes Feature store and ML compute — Flink-based real-time feature pipelines feeding a large-scale MemoryDB cluster; GPU capacity governance and Databricks multi-environment operations for ML training workloads Workflow orchestration and CDC — Airflow-based DAG deployment, change data capture pipeline operations, and data quality monitoring Your Background: 3+ years building and operating production data platform infrastructure at the cluster or platform level, across Spark, Flink, Kinesis, Kubernetes, or equivalent Deep experience in at least one of: Spark-on-K8s cluster operations, Rust-based data or systems engineering, Kubernetes platform engineering and IaC, or data catalog and governance tooling Production AWS experience or equivalent: EKS, S3, Kinesis, and mu

We're looking for an ML Data & Platform Engineer to own the infrastructure that powers our speech AI models: the pipelines that source and prepare training data, and the platform that trains, evaluates, and serves them in production. Speech AI has a data problem most ML teams don't, and you'll be at the centre of solving it, working as part of our ML team to remove friction across the entire lifecycle and get better models into production faster. This is a broad, cross-functional role suited to someone who enjoys working across the full stack: data infrastructure, distributed systems, and production ML, and who takes ownership of problems end to end rather than waiting to be told what to fix. What you'll do Designing, building, and maintaining scalable data pipelines for ingesting, transforming, validating, and storing large datasets used to train our models Developing and maintaining web scraping and data acquisition solutions to keep training datasets fresh, high-quality, and available at scale Building and operating the infrastructure that lets the ML team deploy and evaluate new models quickly, and that serves models efficiently and reliably in production Optimising infrastructure for both iteration speed and production reliability, including GPU utilisation, job scheduling, and training efficiency Implementing observability (monitoring, logging, alerting) across data pipelines and ML systems to catch issues early and keep things running smoothly Troubleshooting complex issues across distributed systems, spanning data infrastructure, training, and inference Continuously improving our data and MLOps practices, and helping shape the roadmap for how our platform evolves as we scale What you'll need Strong proficiency in Python and SQL, with a solid backend or data engineering foundation Hands-on experience with containerisation and orchestration (Docker, Kubernetes), and working with a major cloud provider Experience building data pipelines and ETL/ELT processe

Human Data Quality Engineer (Founding Team) Prolific Prolific isn’t just enabling AI innovation – we’re redefining it. While foundational AI technologies are becoming commoditized, Prolific’s human data infrastructure provides the high-quality, diverse data required to train the next generation of AI models. Through our platform, we empower researchers and companies to access a global, ethically curated participant base, ensuring cutting-edge AI research and training grounded in inclusivity and precision. The Role As one of the founding members of Prolific's newly formed AI Data Services team, you'll help build the quality systems behind some of the world's most advanced AI models. Data quality is a strategic priority for Prolific, so this is a high-visibility role with direct exposure to senior stakeholders. This isn't a traditional QA role. We are not looking for someone to review data against a predefined checklist. We are looking for an innovative thinker that can leverage their expertise to define what good means where no definition exists yet. Acting as a strategic thought partner, you’ll work at the intersection of human data, machine learning, evaluation across frontier use-cases that define what high-quality human data looks like for the next generation of advanced AI. This means that much of the work involves novel problems with no established answer, so you’ll be comfortable working through ambiguity.. . Your primary focus is working directly with clients and alongside frontier AI labs, translating what their models need into robust human data and evaluation strategies. Rather than checking quality at the end of a project, you'll engineer quality into every stage of the lifecycle, from study design and participant strategy through to evaluation, launch readiness and client delivery.You will also work alongside our product engineering, and supply teams to define and build the quality infrastructure that will enable us to deliver h

Senior Human Data Operations Partner, AI Team: Human Data Prolific Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models. The role As a Senior Human Data Operations Partner, you will play a pivotal role in ensuring the smooth execution of end-to-end operational processes that power Prolific's specialised participant pools. You’ll act as a key enabler for client-specific projects, working closely with delivery teams to align participant-related processes with strategic AI customer needs. You’ll also work to transform these processes and workflows into scalable solutions, partnering closely with our product and tech teams to do this. This role demands a hands-on approach to participant onboarding, verification, and quality management, as well as cross-functional collaboration to meet the demands of both internal stakeholders and external clients What you’ll be doing in the role Develop detailed project plans, workflows, and enablement materials to support efficient delivery. Maintain Prolific’s participant pools as high-quality, diverse, and optimised to meet customer demands. Implement and manage verification processes for domain experts and AI taskers, ensuring their skills and qualifications meet client-specific requirements. Continuously optimise participant onboarding and activation funnels to enhance retention and task performance. Monitor participant engagement and quality metrics, using insights to drive improvements. Contribute to upskilling programmes and certification processes for participants involved in complex AI tasks Work closely with product, data, science, and customer success teams to align operational priorities with strategic objec

Human Data Quality Analyst, AI Business Prolific Prolific isn’t just enabling AI innovation – we’re redefining it. While foundational AI technologies are becoming commoditized, Prolific’s human data infrastructure provides the high-quality, diverse data required to train the next generation of AI models. Through our platform, we empower researchers and companies to access a global, ethically curated participant base, ensuring cutting-edge AI research and training grounded in inclusivity and precision. The Role Prolific provides the human data that powers the next generation of AI models, working with frontier labs to capture the complex human judgments researchers need to train, evaluate and improve them. As a Human Data Quality Analyst, you'll be on the front line of making sure that data captures the right signal and is genuinely good enough to do its job. This isn’t traditional, back-office QA. You’ll be doing real analytical work: digging into datasets, identifying patterns and failure modes, investigating why quality has shifted, and turning complex findings into clear insights that help us improve how data is collected, reviewed and delivered. You'll spend real time reading annotations closely, but that is how you gather evidence, not what you produce. What you produce is analysis, practical recommendations and better quality controls. You'll get hands-on exposure to human data, annotation, machine learning pipelines and AI evaluation, working alongside Quality, Engineering, Operations and Delivery on new and evolving problems. There won't always be an established playbook. You'll be guided by our quality engineers, but you'll also need to run your own analysis, test your assumptions and recognise when you need input. It is a role with a steep learning curve from day one and a strong opportunity for someone early in their career to build deep, practical experience in a fast-moving area of AI. What You’ll Be Doing Run day-to-day qualit