Machine Learning Engineer & Data Scientist

HCLTech; Hyderabad, Bengaluru; Hybrid; INR 1,800,000 - 2,600,000; Full time

Job summary

HCLTech in Hyderabad/Bangalore seeks a data-focused ML engineer with ~7 years of experience for end-to-end data science tasks. You will clean and transform data, perform EDA, and build scalable ML models using Python and SQL.

Responsibilities include model development, evaluation, deployment, and creating actionable visualizations with Tableau/Power BI. Collaboration across engineering and product teams is essential.

Qualifications

• Proficient in Python and SQL with hands-on ML experience.
• Hands-on with Scikit-learn, TensorFlow, Keras or PyTorch.
• Strong data analysis using Pandas, NumPy, and Matplotlib.
• Experience in statistical methods and hypothesis testing.
• Familiarity with cloud platforms for model deployment.

Responsibilities

• Data Preparation & Analysis: gather, clean, preprocess data and perform EDA.
• Machine Learning Model Development: build, train, and fine-tune models.
• Model Evaluation & Optimization: assess using accuracy, precision, recall, F1-score, AUC-ROC.
• Data Visualization & Reporting: create dashboards and present insights.
• Collaboration & Stakeholder Communication: work with cross-functional teams.
• Data Pipeline & Automation: design scalable pipelines with Airflow/Kafka/Celery.
• Research & Continuous Learning: stay updated on ML trends and tools.

Skills

Python

SQL

Machine Learning

Data Analysis

Statistics

Cloud Platforms

Tools

Pandas

NumPy

Matplotlib

Seaborn

Plotly

Scikit-learn

TensorFlow

Keras

PyTorch

Tableau

Power BI

Airflow

Apache Kafka

Celery

AWS

Azure

Google Cloud

AWS SageMaker

Azure ML

Job description

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.