Senior MLOps Engineer
Primary Skills
- Skills – 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).
Job requirements
- Years of experience- 6+ Years
- Role- Senior MLOps Data Engineer
- 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, Bantom), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio.
- Key Responsibilities: Model Development & Implementation: Design, develop, and implement machine learning models and statistical algorithms to solve business problems (e.g., predictive modeling, classification, recommendation systems, NLP).
- Own the end-to-end lifecycle of model development: from data exploration and feature engineering to model training, deployment, and monitoring.
- Data Analysis & Insights Generation: Perform in-depth exploratory data analysis (EDA) to identify trends, patterns, and opportunities. Translate complex data into clear and actionable business insights.
- Business Collaboration: Partner with stakeholders (Product, Engineering, and Business teams) to understand requirements and deliver impactful data science solutions.
- Communicate findings and model outcomes effectively to technical and non-technical audiences.
- Technical Leadership: Guide and mentor junior data scientists in best practices, model optimization, and advanced techniques.
- Act as a thought leader, contributing to the strategic roadmap of data science initiatives.
- Scalability and Deployment: Collaborate with Data Engineers and ML Engineers to productionize models using cloud platforms (AWS, GCP, Azure) and MLOps frameworks. Ensure models are robust, scalable, and integrated with business workflows.
- Continuous Improvement: Monitor model performance post-deployment and iterate on models to improve accuracy and business value. Stay up-to-date with the latest tools, techniques, and trends in machine learning and data science.
- Key Skills Machine Learning (Supervised/Unsupervised) Python/R, TensorFlow, PyTorch, Scikit-learn Data Analysis and Feature Engineering SQL, Spark, Big Data Technologies Cloud Platforms (AWS, Azure, GCP) A/B Testing and Statistical Analysis Excellent Communication and Collaboration Skills
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