Specialist I – Data Science

Job DescriptionWe are seeking a motivated Data Scientist with 2–3 years of experience in data analysis, machine learning, and predictive modeling. The ideal candidate will have hands-on experience with Python, SQL, and machine learning libraries to develop data-driven solutions and generate actionable insights.

The candidate will collaborate with cross-functional teams to understand business requirements, perform exploratory data analysis, build and evaluate machine learning models, and support the development of AI/ML and analytics solutions.

Key Responsibilities

• Collect, clean, merge, and transform data from multiple sources for analysis.
• Perform exploratory data analysis (EDA), data wrangling, and feature engineering.
• Develop, train, evaluate, and optimize machine learning models using Python.
• Apply machine learning techniques such as regression, classification, clustering, time series analysis, and ensemble methods.
• Use Python libraries such as Pandas, NumPy, Scikit-learn, and other relevant ML frameworks.
• Write and optimize SQL queries to extract and analyze data.
• Collaborate with business stakeholders and technical teams to understand requirements and develop analytical solutions.
• Support the development and implementation of AI/ML solutions, including Generative AI applications where applicable.
• Document analytical workflows, model results, and technical specifications.
• Monitor model performance and contribute to continuous improvement initiatives.
• Participate in code reviews, testing, and Agile development activities.
• Stay updated on emerging data science techniques, tools, and industry best practices.

Required Skills

• 2–3 years of experience in Data Science, Machine Learning, or Data Analytics.
• Strong programming skills in Python and SQL.
• Hands-on experience with Pandas, NumPy, and Scikit-learn.
• Good understanding of machine learning algorithms and statistical concepts.
• Experience in data preprocessing, exploratory data analysis, and feature engineering.
• Knowledge of model evaluation, validation, and performance optimization.
• Strong analytical thinking, problem-solving, and troubleshooting skills.
• Good communication and collaboration skills.
• Familiarity with Git and version control systems.

Good-to-Have Skills

• Exposure to deep learning frameworks such as TensorFlow or PyTorch.
• Basic knowledge of Generative AI, Large Language Models (LLMs), prompt engineering, and Retrieval-Augmented Generation (RAG).
• Familiarity with PySpark and distributed data processing.
• Exposure to cloud platforms such as Microsoft Azure, AWS, or Google Cloud Platform.
• Understanding of CI/CD pipelines, automation, and model deployment practices.
• Familiarity with data visualization tools and business intelligence platforms.
• Exposure to time series forecasting and advanced analytics techniques.

Qualification

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.