A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe.
You'll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio; including Software and Red Hat.
Curiosity and a constant quest for knowledge serve as the foundation to success in IBM Consulting. In your role, you'll be encouraged to challenge the norm, investigate ideas outside of your role, and come up with creative solutions resulting in ground breaking impact for a wide network of clients. Our culture of evolution and empathy centers on long-term career growth and development opportunities in an environment that embraces your unique skills and experience.
Who you are: As a senior Data Scientist specializing in Artificial Intelligence (AI) and Machine Learning (ML), with experience in developing, training, and deploying AI/ML models for large-scale enterprise applications. Possess strong foundation in data science, predictive analytics, NLP, and deep learning and are skilled at handling big data and cloud-based AI solutions.
Ability to analyze complex datasets, build AI-driven insights, and collaborate with cross-functional teams enables you to contribute to migration from IIAS to Cloudera Data Lake. Proficient in using AI to enhance data quality, automation, and decision-making within enterprise ecosystems.What you’ll do: As a Data Scientist – Artificial Intelligence, you will be responsible for:
AI & Machine Learning Model Development
• Designing and implementing AI/ML models for predictive analytics, anomaly detection, and automation.
• Developing NLP models, recommendation systems, and deep learning algorithms to support business needs.
• Utilizing frameworks like TensorFlow, PyTorch, and Scikit-learn for model training and deployment.
Big Data & Model Deployment
• Deploying and optimizing ML models on Cloudera Machine Learning (CML) and integrating them with Apache Spark and PySpark.
• Working with Kafka and Iceberg tables to build scalable AI-driven data pipelines.
• Ensuring AI models are optimized for performance, scalability, and reliability in a distributed environment.
Data Quality & Virtualization
• Implementing AI-based data quality monitoring using Talend Data Quality (DQ).
• Supporting metadata management, data lineage tracking, and AI-powered automation for data validation.
• Leveraging Denodo for AI-driven data virtualization and federated learning across multiple sources.
Security & Compliance
• Ensuring AI solutions comply with Bank/regulatory regulations and enterprise security standards.
• Implementing AI-driven encryption and anomaly detection models using Thales CipherTrust.
Collaboration & Documentation
• Working closely with data engineers, architects, and business analysts to align AI models with business objectives.
• Documenting model workflows, experiment results, and optimization strategies for reproducibility.
• Supporting AI-driven reporting and visualization using Qlik Sense/Tableau.
6-10 years of experience in AI, Machine Learning, and Data Science.
• Strong expertise in Python, R, SQL, and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience in big data platforms (Cloudera, Apache Spark, Kafka, Iceberg).
• Experience with NLP, deep learning, and AI automation.
• Knowledge of data governance, metadata management, and AI for data quality.
• Familiarity with GitLab CI/CD, Sonatype Nexus, and CheckMarx for AI model deployment.
• Strong problem-solving and analytical skills with the ability to handle large datasets.
Experience in AI/ML for banking and financial services.
• Knowledge of cloud AI platforms (AWS SageMaker, Azure ML, or GCP Vertex AI).
• Familiarity with AI ethics, explainable AI (XAI), and bias detection in models.
• Exposure to graph databases (DGraph Enterprise) for AI-driven insights.