Mega walkin drive - aiml

HCLTech Mega Walk in Drive

Date : Aug 22

Location - Bangalore

Hyderabad

Chennai

Experience - 5 years to 13 years

Please mark your attendance for the Walkin Drive below

QRCode for Attendance Confirmation- Mega Drive - 22nd Aug'26- Data& AI (1) 2.png

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, Num Py, 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, Tensor Flow, Keras, or Py Torch . 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

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Required Skill

s: Programming Languages: Strong proficiency in Python, with experience in SQ L. Machine Learning: Hands-on experience with Scikit-learn, Tensor Flow, Keras, Py Torch, or similar ML librarie s. Data Analysis: Strong skills in Pandas, Num Py, and Matplotlib for data manipulation and analysi s. Statistical Analysis: Experience applying statistical methods to data, including hypothesis testing and regression analysi s. 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 visualization s. SQL & Databases: Proficiency in SQL for querying relational databases and working with No SQL databases (e.g., Mongo DB, Big Query ). Version Control: Experience using Git for version contro

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Desirable Skil

ls: Big Data Technologies: Familiarity with tools like Apache Hadoop, Spark, Dask, or Google Big Query for processing large datase ts. Deep Learning: Experience with deep learning frameworks such as Tensor Flow, Py Torch, or MXN et. NLP & Computer Vision: Experience with natural language processing (NLP) using spa Cy, NLTK, or transformers, and computer vision using Open CV or Tensor Fl ow. MLOps: Familiarity with MLOps tools like Kubeflow, MLflow, or DVC for managing model workflo ws. Data Engineering: Experience with ETL tools like Apache Airflow, Talend, AWS Glue, or Google Dataflow for data pipeline automati

on.

Tools & Technolog

ies: Machine Learning: Scikit-learn, Tensor Flow, Py Torch, Keras, XGBo ost. Data Analysis: Pandas, Num Py, Matplotlib, Seaborn, Plo tly. Cloud Platforms: AWS, Google Cloud, Az ure. Databases: My SQL, Postgre SQL, Mongo DB, Big Query, Snowfl ake. Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plo tly. Version Control:

Git.