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Data Science Lead - R01561319

📅 Feb 18
📍 🇮🇳 Bengaluru - Hybrid🎺 Lead🧬 Data Scientist💼 Full-Time
Hypothesis TestingT-testsZ-testsLinear And Logistic Regression
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📜 Description

  • Lead architectural design of autonomous agents for incident triage, automated investigation, and proactive threat hunting.
  • Build reusable event-driven, WebSocket, and REST integration patterns across connected applications.
  • Design chatbot interfaces and real-time transcription services for voice-activated incident summaries.
  • Partner with data scientists to fine-tune LLMs and optimize RAG pipelines grounded in enterprise data.
  • Build multi-agent orchestration, API and webhook integration, and speech-to-text and language-understanding capabilities.
  • Ensure the architecture supports high-concurrency, low-latency operations and safe, auditable AI actions.

🛠️ Requirements

  • Data science expertise, including statistical analysis, hypothesis testing, regression, forecasting, and classification.
  • Programming and analytics experience with Python/PySpark, SAS/SPSS, and R/R Studio.
  • Machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, CNTK, Keras, or MXNet.
  • Experience with probabilistic graphical models, model monitoring tools, and ML deployment tools such as Kubeflow or BentoML.
  • Knowledge of agentic AI, multi-agent frameworks, LLM fine-tuning, and retrieval-augmented generation.
  • Knowledge of APIs, webhooks, event-driven integrations, WebSockets, speech-to-text, and natural language understanding.
Full job description
Data Science Lead

Primary Skills

  • Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio

    • Specialization

  • Data Science Advanced: Data Scientist

    • Job requirements

  • Job Description: Lead Solution Architect (Agentic AI & App Integration) Role Overview As the Lead Solution Architect, you will be the visionary and technical engine behind our AI-Enabled application. Transform a reactive monitoring environment into a proactive, agentic ecosystem. You will design the foundational integration blocks that allow Generative AI agents to interact with third-party applications, transcribe real-time communications, and provide intelligent, multi-step reasoning to support mission-critical decisions. ________________________________________ The Foundational Blocks 1. Orchestration Layer: Building the multi-agent framework (e.g., AutoGen, LangChain, or Semantic Kernel) that allows AI agents to collaborate, hand off tasks, and resolve complex incidents autonomously. 2. Universal Integration Fabric: Designing a standardized API and Webhook gateway that connects apps for real-time data ingestion and action execution. 3. The "Live Intelligence" Pipeline: Implementing Speech-to-Text (STT) and Natural Language Understanding (NLU) systems to ingest radio, video, and voice comms directly into the AI’s reasoning engine. 4. Governance & ALA (Agentic Level Agreements): Establishing the guardrails, audit logs, and "human-in-the-loop" protocols to ensure AI actions are safe, compliant, and transparent. ________________________________________ Key Responsibilities • Drive Agentic AI Strategy: Lead the architectural design of autonomous agents capable of L1/L2 incident triage, automated investigation, and proactive threat hunting. • Cross-App Integration: Develop reusable integration patterns (Event-Driven, WebSockets, REST) to ensure the app is the "single pane of glass" for all connected applications. • Conversational AI & STT: Design high-fidelity chatbot interfaces and real-time transcription services that allow operators to "talk to the data" and receive voice-activated summaries of active incidents. • Data Science Leadership: Partner with Data Scientists to fine-tune LLMs, optimize Retrieval-Augmented Generation (RAG) pipelines, and ensure model outputs are grounded in enterprise-specific data. • Scalability & Resilience: Ensure the architecture supports high-concurrency, low-latency operations.
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