Machine Learning Engineer, Data Scientist, or Data Analyst

HCLTech; Bengaluru; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

HCLTech is seeking a skilled Machine Learning Engineer, Data Scientist, or Data Analyst to design, develop, and deploy machine learning models and derive actionable insights from diverse data sources.

Ideal candidates will have 8-12 years of experience, strong Python/SQL skills, and hands-on experience with ML libraries, cloud platforms, and data visualization tools. Collaboration with cross-functional teams is expected.

Qualifications

• Proficient in Python with strong SQL skills.
• Hands-on experience with ML libraries and model development.
• Expertise in data wrangling, EDA, and feature engineering.
• Ability to build and evaluate models using standard metrics.

Responsibilities

• Data preparation, cleaning, and preprocessing across sources.
• Develop, train, and fine-tune ML models (supervised/unsupervised).
• Deploy models to production and monitor performance.
• Create dashboards and reports to communicate insights.

Skills

Python

SQL

ML frameworks

Data Analysis

Cloud

Data Visualization

Git

Tools

Scikit-learn

TensorFlow

PyTorch

Keras

XGBoost

Pandas

Job description

HCLTech is a global technology company, home to more than 226,500+ people across 60 countries, delivering industry-leading capabilities centered around AI, digital, engineering, cloud and software, powered by a broad portfolio of technology services and products. We work with clients across all major verticals, providing industry solutions for Financial Services, Manufacturing, Life Sciences and Healthcare, High Tech, Semiconductor, Telecom and Media, Retail and CPG, Mobility and Public Services.

HCLTech is 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.

Exp: 8- 12 years

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.
• 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 techniques such as 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.

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.
• 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.

Tools & Technologies

• Machine Learning: Scikit-learn, TensorFlow, PyTorch, Keras, XGBoost.
• Data Analysis: Pandas, NumPy, Matplotlib, Seaborn, Plotly.
• Data Visualization: Tableau, Power BI, Matplotlib, Seaborn, Plotly.
• Version Control: Git.