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Open nowPosted yesterday

Data Scientist-Advanced Analytics

IBM1,900 open roles

Where
Islamabad, PK
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Your applicationOpen nowData Scientist-Advanced AnalyticsIBM · Islamabad, PK

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on IBM's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

7.4% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 2 days.

Share of postings closed within
  1. 1.5%1 day
  2. 3.5%3 days
  3. 7.4%7 days
  4. 13.2%14 days
  5. 34.5%30 days
This job: posted yesterday

IBM median: 2 days open

The posting

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

  • Develop and deploy machine learning, deep learning, and generative AI solutions.
  • Design and implement agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or similar platforms.
  • Build and orchestrate multi-agent workflows, integrating AI agents with enterprise applications, APIs, and data sources.
  • Develop RAG (Retrieval-Augmented Generation) solutions using vector databases, embeddings, and enterprise knowledge repositories.
  • Fine-tune, evaluate, and optimize foundation models and LLM-based applications.
  • Programming Languages: Exposure to programming languages, particularly Python, and development environments like PyCharm, VS Code, and Jupyter Notebooks

Generative & Agentic AI

  • LLMs (GPT, Claude, Llama, Mistral, etc.)
  • Prompt Engineering and RAG
  • Agentic AI frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel)
  • Multi-agent orchestration and workflow design
  • MCP (Model Context Protocol) and agent integration patterns
  • AI evaluation, observability, and governance

Traditional AI & Data Science

  • Machine Learning and Deep Learning
  • Statistical Modeling and Predictive Analytics
  • NLP, Classification, Clustering, Time Series Forecasting
  • Feature Engineering and Model Optimization
  • Python (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow)

Data & Cloud Technologies

  • SQL, NoSQL, Vector Databases
  • Data Engineering and ETL/ELT concepts
  • Azure AI, Databricks, AWS, or GCP AI Services
  • MLOps / LLMOps, CI/CD, Containerization
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