ML, MLOps, statistical machine learning, traditional ML , predictive modelling, classical ML models (such as bagging, boosting, regression, classification, etc.), data visualization, and Python (with pandas and PySpark SQL expertise as mandatory).
Experience required: 5 - 10 Years
Location: Bangalore/Pune
Specialization
Data Science Advanced: AI/ML Engineer
Job requirements
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),
Drift Frame Work : Framework for detecting drift Automatically monitor track accuracy and trigger model retraining and notifications to restore previous accuracy levels
ML Generalist: Data Scientist with MLOPS Development and maintenance of ML pipeline ML Engineer focusing on experimentation and tracking
Responsibilities: Model Development: Develop machine learning models and algorithms to solve business problems, leveraging techniques such as supervised learning, unsupervised learning, and deep learning.
Deployment and Integration: Deploy machine learning models into production environments and integrate them with existing systems and workflows.
Performance Optimization: Optimize machine learning models for scalability, efficiency, and performance, considering factors such as latency, throughput, and resource utilization.
Monitoring and Maintenance: Monitor model performance in production, identify and diagnose issues, and implement solutions to ensure continued reliability and effectiveness.
Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and product managers, to understand business requirements and deliver solutions that meet stakeholders' needs.
Research and Innovation: Stay up-to-date with the latest advancements in artificial intelligence and machine learning research, and explore new techniques and methodologies to improve model performance and capabilities.
