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Job location - Bengaluru, Hyderabad and Chennai

Job Overview:

We are seeking a skilled Machine Learning Engineer, Data Scientist, or Data Analyst to design, develop, and deploy machine learning models, conduct deep data analysis, and generate actionable insights. The ideal candidate will have experience in data preprocessing, feature engineering, model development, and performance optimization, working with large datasets and leveraging advanced machine learning frameworks.

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.
• SQL & Databases: Proficiency in SQL for querying relational databases and working with NoSQL databases (e.g., MongoDB, BigQuery).
• Version Control: Experience using Git for version control.

Desirable Skills:

• Big Data Technologies: Familiarity with tools like Apache Hadoop, Spark, Dask, or Google BigQuery for processing large datasets.
• Deep Learning: Experience with deep learning frameworks such as TensorFlow, PyTorch, or MXNet.
• NLP & Computer Vision: Experience with natural language processing (NLP) using spaCy, NLTK, or transformers, and computer vision using OpenCV or TensorFlow.
• MLOps: Familiarity with MLOps tools like Kubeflow, MLflow, or DVC for managing model workflows.
• Data Engineering: Experience with ETL tools like Apache Airflow, Talend, AWS Glue, or Google Dataflow for data pipeline automation.

Tools & Technologies:

• Machine Learning: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost.
• Data Analysis: Pandas, NumPy, Matplotlib, Seaborn, Plotly.
• Cloud Platforms: AWS, Google Cloud, Azure.
• Databases: MySQL, PostgreSQL, MongoDB, BigQuery, Snowflake.
• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly.
• Version Control: Git.