Full-Time
Manager – Data Scientist
CEE Business Intelligence
Posted on 9/18/2025
Red Hat
10,001+ employees
Open-source enterprise software platform and services.
No salary listed
Pune, Maharashtra, India
In Person
 Data & Analytics (1) |
|---|
- Proficiency in utilizing LLMs (e.g., Google Gemini), as relevant, for tasks such as brainstorming solutions, deep research, summarizing technical documentation, drafting communications, summarizing complex technical information, and enhancing problem-solving efficiency across the development lifecycle.
- Hands-on experience in a data science or machine learning role.
- Experience in a leadership or management capacity, directly managing a team of data scientists or analysts.
- Demonstrated experience in successfully delivering machine learning models that solve real-world business problems and generate measurable business impact.
- Lead the Data Scientists team
- Oversee the day-to-day activities of technical teams, consisting of varied level of experienced Data Scientists
- Promote and guide continuous professional and personal development of all team members
- Provide guidance to associates within the established company policies; recommend changes to policies and procedures
- Improve knowledge management, promote customer-centric mindset and enhance problem solving capabilities of the team
- Manage the internal stakeholders requirements and expectations about complex projects and initiatives related to data and Artificial Intelligence (AI)
- Lead, coordinate and contribute to the improvement initiatives on CEE (Customer Experience and Engagement) department processes and workflow that aligns with Business Intelligence team's expectations
- Receive assignments in the form of objectives and define how to use resources to meet schedules and goals
- Follow processes and operational policies in selecting methods and techniques for obtaining solutions
- Act as an adviser to our leaders as well as associates to meet schedules and resolve business problems
- Drive the programs or initiatives related to Business Intelligence (e.g. Customer Intelligence)
- Prepare a program plan for Business Intelligence programs
- Identify the resources from different teams to collaborate
- Assist with forecasting, workforce, and capacity planning
- Assist with regular operational reporting
- Provide input to develop processes and procedures as part of the leadership team and co-ordination with other global support centres
- Collaborate with Product Engineering or Business Units, Quality Engineering, Documentation group as well as field teams like Sales function, Solution Architects or marketing teams as needed.
- Experience in working for a technical support environment, in a technology-oriented company or department
- Knowledge of support systems and tools
- Appreciation and passion for open source software
Red Hat provides open-source software and services for large organizations, focusing on cloud-native infrastructure and application management. Its flagship OpenShift is a Kubernetes-based platform that lets enterprises deploy, manage, and scale containerized apps across multiple clouds. It offers a marketplace of certified enterprise software and professional services under a subscription model with updates and support. Its goal is to help enterprises modernize IT infrastructure across clouds while avoiding vendor lock-in.
Company Size
10,001+
Company Stage
Acquired
Total Funding
$34B
Headquarters
Raleigh, North Carolina
Founded
1993
Simplify's Take
What believers are saying
- OpenShift Virtualization is a migration path for legacy VMware and RHV estates.[4][5]
- Red Hat AI and OpenShift AI target enterprise inference, autoscaling, and model serving.[1][4]
- Project Lightwell and Open Accelerator expand security and startup ecosystem pull.[5]
What critics are saying
- VMware pricing pressure keeps virtualization migrations highly competitive.[4][5]
- OpenShift AI depends on external model repositories and storage partners.[1]
- AI-assisted vulnerability discovery increases patch burden across Red Hat’s open-source stack.[5]
What makes Red Hat unique
- Red Hat sells enterprise open source across Linux, cloud, automation, and AI.[1][4]
- OpenShift unifies application, virtualization, and container management across hybrid environments.[4][5]
- Ansible and OpenShift give Red Hat control points across infrastructure and operations.[1][4]
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Benefits
Health Insurance
Dental Insurance
Vision Insurance
401(k) Retirement Plan
401(k) Company Match
Paid Vacation
Paid Sick Leave
Paid Holidays
Parental Leave
Family Planning Benefits
Tuition Reimbursement
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6 month growth
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↑ 0%RHEL AI 3.4 Released on AWS Marketplace: What's New and How to Upgrade. Red Hat Enterprise Linux AI 3.4.0 is now available on AWS Marketplace. Here is what changed, how to upgrade running instances, and what the new version means for enterprise AI workloads. Red Hat just shipped RHEL AI 3.4.0 (build 20260602-1780387621) on AWS Marketplace. If you are already running RHEL AI on EC2, your existing instances are unaffected - but upgrading is recommended. What is RHEL AI. Red Hat Enterprise Linux AI is a purpose-built operating system image that packages everything you need to run AI inference and fine-tuning workloads: * InstructLab for synthetic data generation and model alignment * vLLM for high-throughput inference serving * Granite models pre-loaded and ready to serve * GPU drivers (NVIDIA CUDA) pre-configured * SELinux and container isolation enabled by default It ships as an AMI on AWS Marketplace, a QCOW2 for on-prem, and ISOs for bare-metal deployment. What changed in 3.4. While Red Hat has not published detailed release notes for this specific build, version 3.4 follows the rapid cadence established since RHEL AI 1.1 in late 2024. Based on the Red Hat AI platform roadmap shared at Summit 2026: * InstructLab updates - improved synthetic data generation pipelines and model evaluation tooling * vLLM performance - latest upstream vLLM with better throughput on NVIDIA GPUs * Security patches - RHEL 9 base layer with all current CVE fixes * Granite model refresh - newer model checkpoints with improved reasoning capabilities * bootc image mode - immutable OS updates via bootc upgrade for atomic rollbacks How to Upgrade on AWS. Option 1: launch a new instance. The simplest path - launch the new AMI and migrate your workload: # Find the new RHEL AI 3.4 AMI from Marketplace aws ec2 describe-images \ -owners aws-marketplace \ -filters "Name=name,Values=*RHEL-AI*3.4*" \ -query 'Images.{ID:ImageId,Name:Name,Date:CreationDate}' \ -output table Option 2: in-place upgrade with bootc. If your instance supports image mode (RHEL AI 1.3+): # SSH into your running instance ssh -i your-key.pem cloud-user@your-instance-ip # Check current version cat /etc/redhat-release ilab -version # Pull and stage the new image sudo bootc upgrade # Reboot to apply sudo systemctl reboot After reboot, verify: cat /etc/redhat-release rpm -q vllm instructlab Option 3: keep running (no action required). Your existing subscription and running instances are unaffected. Previous versions remain accessible. Only upgrade when your maintenance window allows. SSH access reminder. RHEL AI on AWS uses: * Username: cloud-user (not ec2-user, not root) * Auth: SSH key pair you selected at launch * Root: Disabled by default (use sudo) # Set permissions on your key chmod 400 your-key.pem # Connect ssh -i your-key.pem [email protected] Instance Sizing for RHEL AI. For inference workloads with Granite models: | Instance Type | GPUs | VRAM | Best For | | g5.xlarge | 1x A10G | 24 GB | Small models, development | | g5.12xlarge | 4x A10G | 96 GB | Granite 7B inference | | p4d.24xlarge | 8x A100 | 320 GB | Fine-tuning, large models | | p5.48xlarge | 8x H100 | 640 GB | Production training | Why this matters. RHEL AI is Red Hat's answer to the "AI platform fragility" problem. Instead of stitching together drivers, frameworks, and serving layers yourself, you get a tested, supported stack that upgrades atomically. Combined with InstructLab fine-tuning and Ansible-driven deployments, version 3.4 continues the trajectory toward enterprise AI that is as manageable as any other RHEL workload.
vLLM Inference Optimizations on Red Hat OpenShift AI: from KV Cache to distributed serving. Deep dive into vLLM inference optimizations presented at Red Hat Tech Day Netherlands 2026 - covering KV cache, continuous batching, quantization (W4A16, W8A8), distributed inference with Tensor Parallelism, and real-world benchmarks showing 75% memory reduction with only 1.5% precision loss. At the Red Hat Tech Day Netherlands (June 2026), a Red Hat engineer delivered a comprehensive deep dive into vLLM inference optimizations - from fundamental KV caching through quantization to distributed multi-node serving. This was not a marketing talk; it was a production engineering session with real benchmarks, real cluster configurations, and real model deployment data. I captured the entire session and here is everything you need to know about running LLMs efficiently on OpenShift AI. vLLM: two core concepts. The talk structured vLLM around two pillars: * Inference Optimizations - Making individual model serving faster and leaner * Distributed Inference - Scaling across multiple GPUs and nodes KV Cache: the foundation of fast inference. Every transformer token generation requires attending to all previous tokens. Without optimization, this means recomputing the full attention matrix every step. KV Cache stores the Key and Value tensors from previous tokens, avoiding redundant computation for the prefix. This is the single most impactful optimization for autoregressive generation. Static vs continuous batching. * Static batching: All sequences in a batch must wait for the longest to complete * Continuous batching: Fill empty slots with new sequences as others finish - dramatically improving throughput vLLM implements continuous batching natively, ensuring GPU utilization stays high even with variable-length requests. Pre-Optimized models. vLLM comes with first-class optimization support for: * Llama (Meta) * Qwen (Alibaba) * Gemma (Google) * Mistral * DeepSeek * Phi (Microsoft) * Molmo * Granite (IBM/Red Hat) * Nemotron (NVIDIA) Quantization: fit more, lose almost nothing. Supported formats. | Format | Description
Ansible Automation Intelligent Assistant and MCP Server: AI-Driven operations with BYOK RAG. Red Hat introduces the Automation Intelligent Assistant (AIA) with Bring-Your-Own-Knowledge RAG, a hosted MCP server exposing AAP capabilities to LLMs, and BYOM provider flexibility across Red Hat AI, OpenAI, Azure, watsonx, and Gemini. Here's the full architecture from Red Hat Tech Day Netherlands 2026. At Red Hat Tech Day Netherlands (June 2026), the Ansible team unveiled three AI capabilities that fundamentally change how organizations interact with their automation platform: * Automation Intelligent Assistant (AIA) - a chatbot embedded in AAP with BYOK RAG * MCP Server for AAP - expose automation capabilities as declarative endpoints to any LLM * BYOM Provider Matrix - flexible AI backend connectivity Automation Intelligent Assistant (AIA). The Automation Intelligent Assistant is a chat assistant embedded directly into the Ansible Automation Platform UI. It harnesses generative AI to support day-to-day platform management. Core capabilities. * Access to on-demand Ansible expertise - ask questions, get answers with source links * Speeds up troubleshooting, onboarding, and day-to-day administration of Ansible Automation Platform * Includes links for source validation and accelerated learning - never blindly trust AI output * New! "Bring-Your-Own-Knowledge" (BYOK) allows organizations to inject internal policies, best practices, and procedures into RAG pipeline for more relevant and meaningful model responses BYOK: why your knowledge matters. The key insight from the talk: if you bring your own model (BYOM), you also need to bring your own knowledge (BYOK). An LLM knows generic Ansible best practices. But it doesn't know: * Your company's change management procedures * Your specific network topology and naming conventions * Your compliance requirements (PCI-DSS, HIPAA, SOC2) * Your runbook for "what to do when the firewall team says no" BYOK injects this organizational knowledge into the RAG pipeline so the assistant gives answers grounded in YOUR reality, not generic documentation. Introducing MCP Server for Ansible Automation Platform. The hosted MCP server exposes AAP capabilities to LLMs and agentic systems as declarative endpoints: * Provides a standardized interface for securely querying automation infrastructure data and executing workflows * Query facts about your automation environment, execute automation jobs, manage credentials, and more * Simplifies AI integration - enabling existing automation through AAP to be exposed to AI tools without writing custom API code or middleware Available Tool sets. | Tool Set | Capabilities | | Job management | Launch, monitor, cancel jobs | | Inventory management | Query hosts, groups, variables | | System monitoring | Check service health, resource usage | | User management | Query permissions, roles, teams | | Security and compliance | Audit trails, policy checks | | Platform configuration | Settings, credentials, integrations | Traditional vs AI-Driven Automation. Traditional Automation (left side): * Navigate to AAP * Find correct job template / CL * Fill out survey / fields * Submit / kick-off automation * Verify results in AAP AI-Driven Automation with MCP Server (right side): * Navigate to AI chat interface (Claude, ChatGPT, Gemini, etc.) * Request developer VM; respond to requests for additional information * Verify results in AAP Same outcome. Dramatically different experience. The AI handles template selection, parameter filling, and execution - the human just describes what they need. AI-Assisted content creation: BYOM Provider Matrix. AAP supports flexible connectivity with Red Hat AI platforms and BYOM (Bring Your Own Model) providers: Intelligent Assistant (chatbot in AAP UI). | Provider | Availability | | Red Hat AI | AAP 2.6+ | | OpenAI | AAP 2.6+ | | Azure OpenAI | AAP 2.6+ | | IBM watsonx | N/A | | Gemini (Vertex) | Coming Soon | Coding assistant (ansible VS Code extension). | Provider | Availability | | Red Hat AI | AAP 2.6+ | | OpenAI | Coming Soon | | Azure OpenAI | Coming Soon | | IBM watsonx | AAP 2.5+ | | Gemini (Vertex) | AAP 2.6+ | Key takeaway: IBM watsonx was the first external provider for the VS Code coding assistant (AAP 2.5+), while Red Hat AI and OpenAI lead on the intelligent assistant side. EE Builder: Publish to Git and auto-build. The Automation Portal's EE Builder integrates directly with source control: * Publish to a Git repository (GitHub authenticated) * Namespace: test-rhaap-portal * Repository: platform-ops-compliance-ee * Trigger a build of the Execution Environment after publishing - one checkbox enables full CI/CD This means: define your EE in the visual builder | publish to GitHub | automatically build the container image and push to your registry. No manual ansible-builder CLI needed. Requirements. * AAP 2.5+ * Automation portal installation (OpenShift Container Platform OR RHEL VM appliance) * SCM (Git) access Drive automation adoption and scale. The overarching theme: make automation accessible to everyone in the organization, not just the Ansible experts. Through: * Self-service templates * AI-powered assistance * Visual builders * Natural language interfaces Key takeaways. * BYOK is essential - BYOM without BYOK gives you generic answers. Inject your procedures. * MCP Server = zero-code AI integration - expose AAP to any LLM without middleware * Traditional | AI-driven is additive - the MCP server doesn't replace AAP UI, it adds a natural language layer * Provider flexibility - not locked into one AI vendor; swap between Red Hat AI, OpenAI, Azure, watsonx, Gemini * EE Builder | GitHub | Auto-build - complete CI/CD from visual wizard to container registry AIOps Solution Guides: from linear Rules to AI Inference. Red Hat also introduced the Ansible Automation Platform Solution Guides - detailed walkthroughs for production AIOps scenarios. The scaling problem. Traditional event-driven automation (EDA) is deterministic - for every event you want to handle, you write a specific rule and a corresponding action: | Approach | Events | Rules Required | Actions | | Traditional EDA | 10 | 10 | 10 | | Traditional EDA | 100 | 100 | 1,000 | | Traditional EDA | 1,000 | 1,000 | - | | AIOps with EDA | 1,000 | 1 (+ AI inference) | Dynamic | AIOps breaks this linear relationship by inserting AI inference between the event and the action. Instead of hand-coding a rule for every possible failure mode, a single intelligent workflow captures the event, uses AI to diagnose the root cause, and generates the remediation dynamically. Available Solution Guides. * AIOps automation with Ansible - self-healing infrastructure using Event-Driven Ansible, Lightspeed, and AI inference * High-Availability AAP with EDB PostgreSQL DR - multi-datacenter active-passive disaster recovery * Automated Incident Remediation with IBM Instana - closed-loop observability | auto-remediation * Unlock AIOps with ServiceNow LEAP and Ansible MCP server - cut incident MTTR from hours to minutes * AI infrastructure automation with Ansible - provision from GPU instances to serving models using infra.ai and redhat.ai collections * Intelligent Assistant with Red Hat AI Inference Server - deploy self-hosted LLM on RHEL with GPU acceleration Introducing AAP Solution Guides portal. Key highlights: * Detailed AIOps walkthroughs focused on leading partner integrations: IBM Instana, ServiceNow, and Splunk * RAG-enabled (coming soon) - ask questions about the guides using AI
Red Hat launches new developer tools for agentic AI. ANI 26 May 2026, 21:11 GMT+10 New Delhi [India], May 13: Red Hat, the world's leading provider of open-source solutions, today announced expanded capabilities across its developer portfolio specifically built for the requirements of AI agents. Through the newly-available Red Hat Desktop and enhancements to Red Hat Advanced Developer Suite, Red Hat intends to smooth the transition from agents running locally on developer workstations to production-scale deployments across the hybrid cloud. What Red Hat announced With today's general availability of Red Hat Desktop, Red Hat is providing commercial support for the Red Hat build of Podman Desktop, creating a more reliable foundation for local container and AI development. Red Hat Desktop also includes capabilities for isolated AI agent sandboxing, an initiative designed to help developers execute and test autonomous agents in a protected sandbox on their local hardware, preventing unverified agent actions from affecting the host OS. Red Hat Advanced Developer Suite also adds new capabilities, including a trusted software factory, Red Hat Trusted Libraries and AI-driven exploit intelligence to modernize security across the software supply chain. These new features use AI to determine if known vulnerabilities in generated code are relevant to a specific application runtime, allowing developers to prioritize remediation based on actual risk. Why this matters As the volume of AI-generated code increases, developers need a workflow that balances local experimentation with enterprise-grade deployment. Whether developers start locally with Red Hat Desktop or in a cloud-based development environment via Red Hat OpenShift Dev Spaces, they receive the same consistency and governance required for enterprise production. By unifying these environments and transitioning to production scale with Red Hat OpenShift, Red Hat enables teams to treat AI agents as tier-one applications. This approach provides a security-driven path to production, enabling developers to move from experimental local sandboxes to verified, scalable innovation across the hybrid cloud. What Red Hat experts are saying 'The transition to agentic AI expands the requirements for modern application development,' said James Labocki, Senior Director, Product Management, Red Hat. 'By establishing a trusted production path across the hybrid cloud with Red Hat Advanced Developer Suite and providing consistent environments through Red Hat Desktop and Red Hat OpenShift Dev Spaces, The UK News is helping developers accelerate and own their AI strategy with the same rigor they apply to their core IT applications.' Key takeaways - Standardized AI lifecycle: Red Hat provides a more consistent experience from local machines to the cloud, helping organizations move AI from experimental projects to repeatable production workflows.- Maintain developer choice: Red Hat has expanded support in Red Hat OpenShift Dev Spaces, a Red Hat OpenShift feature, to include integration with Amazon Web Services (AWS) Kiro coding assistant (technical preview). This joins existing integration for Microsoft Copilot, Claude CLI and more, providing the flexibility to use preferred coding assistants and environments from local machines.- Shift security left: Built on Red Hat Hardened Images and Red Hat Trusted Libraries (both available with SLSA Level 3 origin and integrity), these tools provide a software supply chain that is transparent and verifiable before code is even written.- Sandbox-first testing: Developers can execute autonomous agents in an isolated environment, providing a safety layer to observe agent behaviors before cluster deployment. Deeper details: Red Hat Desktop and Podman integrationRed Hat Desktop delivers an enterprise-supported environment for local container and AI development centered on the hardened and supported Red Hat build of Podman Desktop. Developers can easily access the full library of Red Hat Hardened Images from their laptop, while connecting to local or remote OpenShift clusters for unit testing. This ensures that the container running on the developer's machine is architecturally consistent with the one running in production. Developers looking to test sandboxed AI agents can find more information at www.openkaiden.ai. Flexible coding assistants Red Hat OpenShift Dev Spaces now provides an extensible framework that allows developers to integrate preferred AI-driven tools directly into their cloud-based IDE. This includes new support for the AWS Kiro coding assistant (technical preview), alongside existing integrations for Microsoft Copilot, Claude CLI, Cline, Continue, Roo and more. By supporting both proprietary and open-source assistants, Red Hat enables teams to use frontier models or host private models, helping to align developer productivity tools with corporate security and sovereignty requirements. Red Hat Advanced Developer Suite enhancementsThe latest version of Red Hat Advanced Developer Suite introduces the developer preview of a trusted software factory based on accepted CNCF best practices and Red Hat's internal build processes. This provides a standards-based CI/CD implementation that customers can use as-is or tweak and replicate to meet specific needs. Additional features include: - Red Hat Trusted Libraries: Curated Python packages built on SLSA Level 3 infrastructure with added software bill of materials (SBOMs) and cryptographic signatures to help provide a more transparent and verifiable software supply chain.- Exploit intelligence: Developed using the NVIDIA AI blueprint for vulnerability analysis, this capability uses AI-driven code reasoning to determine if a vulnerable function is actually reachable in an application's runtime environment. By isolating exploitable code paths from broader vulnerability data, Red Hat helps developers prioritize fixes that actually impact security. Red Hat Summit Join the Red Hat Summit keynotes live on YouTube to hear the latest from Red Hat executives, customers and partners: - The next platform is choice - Tuesday, May 12, 8:30-10 a.m. EDT- The AI-ready enterprise is here - Wednesday, May 13, 9-10 a.m. EDT Learn more: - OpenShift: Consistent integration for the hybrid enterprise- Red Hat Hardened Images Accelerates Cloud-Native Development and Zero-CVE Strategies- Red Hat Desktop brings Kubernetes-aligned development to the desktop- From experimentation to production: Building trust in the agentic AI era- Learn more about Red Hat Summit- See all of Red Hat's announcements this week in the Red Hat Summit newsroom- Follow @RedHatSummit or #RHSummit on X for event-specific updates Connect with Red Hat - Learn more about Red Hat- Get more news in the Red Hat newsroom- Read the Red Hat blog- Follow Red Hat on X- Follow Red Hat on Instagram- Watch Red Hat videos on YouTube- Follow Red Hat on LinkedIn About Red Hat Red Hat is the open hybrid cloud technology leader, delivering a trusted, consistent and comprehensive foundation for transformative IT innovation and AI applications. Its portfolio of cloud, developer, AI, Linux, automation and application platform technologies enables any application, anywhere-from the datacenter to the edge. As the world's leading provider of enterprise open source software solutions, Red Hat invests in open ecosystems and communities to solve tomorrow's IT challenges. Collaborating with partners and customers, Red Hat helps them build, connect, automate, secure and manage their IT environments, supported by consulting services and award-winning training and certification offerings. Forward-Looking Statements Except for the historical information and discussions contained herein, statements contained in this press release may constitute forward-looking statements within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements are based on the company's current assumptions regarding future business and financial performance. These statements involve a number of risks, uncertainties and other factors that could cause actual results to differ materially. Any forward-looking statement in this press release speaks only as of the date on which it is made. Except as required by law, the company assumes no obligation to update or revise any forward-looking statements. Red Hat, Red Hat Enterprise Linux, the Red Hat logo and OpenShift are trademarks or registered trademarks of Red Hat, LLC or its subsidiaries in the U.S. and other countries. Linux is the registered trademark of Linus Torvalds in the U.S. and other countries. (ADVERTORIAL DISCLAIMER: The above press release has been provided by NewsVoir. ANI will not be responsible in any way for the content of the same.)
Red Hat hosts TSANet Europe Focus Group meeting. Red Hat hosted the Europe TSANet Focus Group at its site in Munich, Germany, on May 6-7, 2026. Participants representing HPE, Microsoft, Red Hat, NetApp, Fujitsu, AWS, IBM, Nutanix, F5 Networks, and DriveSavers attended the meeting either in person or virtually. Sovereign support. Red Hat emphasized support as a core contributor to customer value, highlighting sovereign cloud, AI adoption, and virtualization migration as key industry trends. A dedicated legal session examined the complexity of IT sovereignty and its variations across regions. AWS, Microsoft, and NetApp presented their sovereignty strategies, focusing on data residency, resilience, multi-region architectures, flexible cloud deployment options, and on-premises control. 2026 TSANet plan update. TSANet provided an update, explaining that the strategy focuses on the partner collaboration platform. This includes TSANet Connect, the Technology Partner Framework (clarifying collaboration with OEM, solution, and certified partners), and an upcoming expansion into the service provider space. AI in Motion - Discover practical applications for internal processes and customer solutions. The theme "AI for the grind, humans for the gold" focused on AI in technical support. Companies reported declining support volumes as customers increasingly self-solve with AI, while critical cases remain steady. AWS, Red Hat, HPE, Nutanix, and IBM presented AI initiatives, including agentic AI, automated workflows, and embedded assistants designed to boost engineer productivity rather than replace them. Discussions highlighted AI's growing role in case reduction, proactive support, skill-based routing, and customer experience, with consensus that AI will handle routine work while humans focus on complex issues. Next meeting planned - fall 2026. The next TSANet EMEA meeting will be hosted by HPE in Sofia, Bulgaria in fall 2026.