Data Scientist

Job Title - Data Scientist

Minimum years of experience - 5+ years

Responsibilities:

• Leverage strong machine learning and data analysis experience in complex data environments to support business objectives in effective and scalable ways.
• Utilize a solid foundation in mathematics and statistics to guide model development, validation, and performance evaluation.
• Design and develop machine learning and LLM-based solutions, including applications such as RAG pipelines, chatbots, and intelligent assistants where applicable.
• Develop and integrate REST APIs and FastAPI-based services for model deployment and application integration.
• Support implementation of monitoring and observability practices for ML and LLM systems, including performance tracking and response evaluation.
• Build and maintain data pipelines, including data ingestion, preprocessing, transformation, and validation.
• Collaborate effectively within Agile Scrum teams, contributing to iterative development and continuous improvement.
• Document business processes, workflows, models, APIs, and technical implementations clearly and comprehensively.
• Engage with business and technical stakeholders to understand requirements and translate them into data science and AI solutions.
• Participate in collaborative conceptualization sessions to brainstorm and refine ML and LLM-based solutions.
• Assist in implementing agent-based workflows using modern frameworks and support integration with enterprise systems.

Required Skills:

• Proficiency in Python, Machine Learning, REST API development, FastAPI, and SQL.
• Experience with data processing, cleansing, and validation to ensure data quality and integrity for analysis.
• Conduct data quality checks and exploratory data analysis to support model development.
• Demonstrated programming skills in Python and experience in building and integrating APIs.
• Build end-to-end machine learning models, including data preparation, feature engineering, and deployment workflows.
• Strong understanding of statistical modelling techniques such as Regression, Clustering, Decision Trees, and Logistic Regression.
• Familiarity with machine learning algorithms such as KNN, Random Forests, Ensemble Methods, and probabilistic approaches.
• Basic experience with Large Language Models such as GPT, BERT, or similar architectures.
• Understanding of prompt engineering and LLM application usage.
• Familiarity with vector databases, embeddings, and retrieval-based approaches.
• Basic understanding of agent frameworks such as LangChain or Semantic Kernel.
• Knowledge of data mining concepts and experience with data visualization tools and dashboards.