DATA SCIENTIST
Data Scientist in Gurugram building and deploying ML/deep learning models, doing EDA and feature engineering in Python (Pandas, NumPy, PySpark), applying NLP, LLMs and generative AI, and communicating insights via Power BI/Tableau for an IT services company. Requires 3-4 years of hands-on data science experience.
- Compensation
- Not disclosed
- Location
- Gurugram, India
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data science
- Posted
Skills
Must have
- 3-4 years hands-on experience in data science, analytics, or related technical field within the IT Services sector
- Strong proficiency in Python and SQL for data manipulation, querying, and algorithm implementation
- Deep expertise in machine learning, statistics, and data visualization techniques
- Well-versed in core libraries: Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, PyTorch
- Practical experience with NLP, Generative AI, and LLM technologies
- Ability to translate complex technical concepts into business strategies using Power BI and Tableau
- Solid understanding of data analysis methodologies and capacity to work with large, unstructured datasets
- Design, develop, and deploy machine learning models and deep learning architectures for business problems
- Conduct exploratory data analysis (EDA) on large-scale datasets
- Engineer features and optimize data pipelines with Pandas, NumPy, and PySpark
- Perform statistical analysis and hypothesis testing to validate model performance
- Collaborate with cross-functional teams to integrate data science solutions into production
Full description
Summary
We are seeking a skilled Data Scientist to join our IT Services division, where you will play a pivotal role in transforming complex datasets into actionable business insights. This position demands a professional who can leverage advanced statistical methods and machine learning algorithms to solve critical challenges and drive strategic decision-making. The ideal candidate will bridge the gap between raw data and practical applications, ensuring that our analytical models are robust, scalable, and aligned with organizational goals.
Responsibilities
- Design, develop, and deploy sophisticated machine learning models and deep learning architectures to address specific business problems.
- Conduct comprehensive exploratory data analysis (EDA) to uncover hidden patterns, trends, and anomalies within large-scale datasets.
- Engineer features and optimize data pipelines using Python libraries such as Pandas, NumPy, and PySpark to ensure high-quality input for modeling.
- Apply natural language processing (NLP) techniques and work with Large Language Models (LLMs) and Generative AI to enhance text-based analytics and automation.
- Utilize frameworks like TensorFlow, PyTorch, and Scikit-learn to build, train, and validate predictive models and neural networks.
- Create interactive and intuitive dashboards using Power BI and Tableau to communicate findings effectively to stakeholders.
- Perform rigorous statistical analysis and hypothesis testing to validate model performance and ensure data integrity.
- Collaborate with cross-functional teams to integrate data science solutions into production environments and support ongoing model maintenance.
Requirements
Requirements:
- Possess 3 to 4 years of hands-on experience in data science, analytics, or a related technical field within the IT Services sector.
- Demonstrate strong proficiency in Python and SQL for data manipulation, querying, and algorithm implementation.
- Show deep expertise in machine learning, statistics, and data visualization techniques.
- Be well-versed in core libraries including Pandas, NumPy, Scikit-learn, XGBoost, TensorFlow, and PyTorch.
- Have practical experience with NLP, Generative AI, and LLM technologies.
- Display the ability to translate complex technical concepts into clear business strategies using tools like Power BI and Tableau.
- Maintain a solid understanding of data analysis methodologies and the capacity to work with large, unstructured datasets.