Machine Learning Engineer & Data Scientist

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.