Overview

HCLTech Mega Walk in Drive for Machine Learning Engineers, Data Scientists, or Data Analysts. Candidates with 5-13 years of experience are encouraged to apply.

Responsibilities

• 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
• 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
• Evaluate model performance using metrics such as Accuracy, Precision, Recall, F1-Score, AUC-ROC, Confusion Matrix, and Cross-validation
• Improve model performance through feature engineering, data augmentation, and regularization
• Deploy models into production environments and monitor performance for continual improvement
• 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
• 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
• 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
• 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

Qualifications

• Strong proficiency in Python
• Hands-on experience with Scikit-learn, TensorFlow, Keras, PyTorch, or similar ML libraries
• Strong skills in Pandas, NumPy, and Matplotlib for data manipulation and analysis
• Experience applying statistical methods to data, including hypothesis testing and regression analysis
• Familiarity with AWS, Azure, or Google Cloud for deploying models and using cloud-native data services
• Experience using Tableau, Power BI, Matplotlib, Seaborn, or Plotly for creating visualizations
• Proficiency in SQL for querying relational databases and working with NoSQL databases (e.g., MongoDB, BigQuery)
• Experience using Git for version control

Nice to have

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

Education

• Bachelor's or Master's Degree in Computer Science, Statistics, Data Science, or related technical field