AI/ML Data Scientist

3 days ago

Bengaluru, Karnataka, India beBeeArtificialintelligence Full time ₹ 1,50,00,000 - ₹ 2,00,00,000
Job Opportunity

We are seeking a skilled professional to fill the role of Data Science Practitioner.

About the Role:

The selected candidate will be responsible for developing AI/ML-based decision-making frameworks and models for business outcomes.

Key Responsibilities:
  • Develop AI/ML solutions: Formulate, design, and deliver AI/ML-based decision-making frameworks and models for business outcomes.
  • Evaluate Solution Impact: Measure and justify AI/ML-based solution values.
  • Define Models and Workflows: Define models and workflows to meet performance, scalability, and accuracy requirements.
  • Ensure Business Insights: Ensure business insights are actionable and aligned with strategic goals.
  • Deploy AI/ML Solutions: Responsible for the successful deployment of AI/ML solutions on Google Cloud.
Requirements:
  • Subject Matter Expert: Subject Matter Expert (SME) with deep expertise in data science and AI/ML on Google Cloud.
  • Decision-Making Influence: Influence and guide analytical and AI-driven decision-making across multiple teams.
  • Data Science Opportunities: Engage with stakeholders to identify high-impact data science opportunities.
  • Design, Build, and Optimize ML Models: Design, build, and optimize ML models for predictive, prescriptive, and descriptive analytics.
  • Collaboration with Data Engineering Teams: Collaborate with data engineering teams to ensure high-quality data pipelines for ML workloads.
  • Ensure Compliance and Ethics: Ensure solutions adhere to ethical AI principles, security requirements, and compliance standards.
  • Model Deployment and Monitoring: Lead model deployment, monitoring, and continuous improvement in production environments.
Technical Skills:
  • Google Cloud AI/ML Tools: Proficiency in Google Cloud AI/ML tools (Vertex AI, AI Platform, BigQuery ML, TensorFlow, AutoML).
  • Statistical Modeling and Machine Learning Algorithms: Strong understanding of statistical modeling, machine learning algorithms, and data mining techniques.
  • Large-Scale Data Processing Frameworks: Experience with large-scale data processing frameworks (Dataflow, Dataproc) and SQL-based analytics (BigQuery).
  • Programming Languages and ML Frameworks: Proficiency in Python/R for data science, with experience in ML frameworks (TensorFlow, Scikit-learn, PyTorch).
  • MLOps Practices: Knowledge of MLOps practices, including CI/CD for ML and model governance.

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