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Job Description:
Key Responsibilities
Develop, train, and evaluate machine learning models using Python, scikit-learn, and related libraries.
Design and build robust data pipelines and workflows leveraging Pandas, SQL, and Kedro.
Create clear, reproducible analysis and reports in Jupyter Notebooks.
Integrate machine learning models and data pipelines into production environments on AWS.
Work with Langchain to build applications leveraging large language models and natural language processing workflows.
Collaborate closely with data engineers, product managers, and business stakeholders to understand requirements and deliver impactful solutions.
Optimize and monitor model performance in production and drive continuous improvement.
Follow best practices for code quality, version control, and documentation.
Required Skills and Experience
7+ years of professional experience in Data Science, Machine Learning, or a related field.
Strong proficiency in Python and machine learning frameworks, especially scikit-learn.
Deep experience working with data manipulation and analysis tools such as Pandas and SQL.
Hands-on experience creating and sharing analyses in Jupyter Notebooks.
Solid understanding of cloud services, particularly AWS (S3, EC2, Lambda, SageMaker, etc.).
Experience with Kedro for pipeline development and reproducibility.
Familiarity with Langchain and building applications leveraging LLMs is a strong plus.
Ability to communicate complex technical concepts clearly to non-technical audiences.
Strong problem-solving skills and a collaborative mindset.
Nice to Have
Experience with MLOps tools and practices (model monitoring, CI/CD pipelines for ML).
Exposure to other cloud platforms (GCP, Azure).
Knowledge of data visualization libraries (e.g., Matplotlib, Seaborn, Plotly).
Familiarity with modern LLM ecosystems and prompt engineering.
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