Machine Learning Engineer & Data Scientist

Job description

Experince-7 Yrs Job Location- Hyderabad/ Bangalore Key Responsibilities: • Data Preparation & Analysis: • Gather, clean, and preprocess structured, semi-structured, and unstructured data from various sources. • Conduct exploratory data analysis (EDA) to identify trends, patterns, and outliers. • Apply data wrangling techniques using Pandas, NumPy, and SQL to transform raw data into usable formats. • Use statistical analysis to drive data-driven decision-making. • Machine Learning Model Development: • Build, train, and fine-tune machine learning models using Scikit-learn, TensorFlow, Keras, or PyTorch. • Develop predictive models, classification algorithms, clustering models, and recommendation systems. • Conduct hyperparameter optimization using techniques like grid search or random search. • Model Evaluation & Optimization: • Evaluate model performance using metrics such as Accuracy, Precision, Recall, F1-Score, AUC-ROC, Confusion Matrix, and Cross-validation. • Improve model performance through techniques such as feature engineering, data augmentation, and regularization. • Deploy models into production environments, and monitor performance for continual improvement. • Data Visualization & Reporting: • Develop dashboards and reports using Tableau, Power BI, Matplotlib, Seaborn, or Plotly. • Present findings through clear visualizations and actionable insights to non-technical stakeholders. • Write detailed reports on data analysis and machine learning results, ensuring transparency and reproducibility. • Collaboration & Stakeholder Communication: • Work closely with cross-functional teams (e.g., engineering, product, business) to define data-driven solutions. • Communicate technical concepts clearly to non-technical stakeholders and provide insights that influence product and business strategy. • Data Pipeline & Automation: • Design and implement scalable data pipelines for model training and deployment using Airflow, Apache Kafka, or Celery. • Automate data collection, preprocessing, and feature extraction tasks. • Research & Continuous Learning: • Stay up-to-date with the latest trends in machine learning, deep learning, and data science methodologies. • Explore new tools, techniques, and frameworks to improve model accuracy and efficiency. Required Skills: • Programming Languages: Strong proficiency in Python, with experience in SQL. • Machine Learning: Hands-on experience with Scikit-learn, TensorFlow, Keras, PyTorch, or similar ML libraries. • Data Analysis: Strong skills in Pandas, NumPy, and Matplotlib for data manipulation and analysis. • Statistical Analysis: Experience applying statistical methods to data, including hypothesis testing and regression analysis. • Cloud Platforms: Familiarity with AWS, Azure, or Google Cloud for deploying models and using cloud-native data services (e.g., AWS Sagemaker, Azure ML). • Data Visualization: Experience using Tableau, Power BI, Matplotlib, Seaborn, or Plotly for creating visualizations.