Data Scientist

Role Summary

A Data Scientist with 1–3 years of experience is responsible for collecting, analyzing, and interpreting large datasets using advanced analytics, machine learning, and cloud-based technologies. The role focuses on deriving actionable insights, building predictive models—including those using large language models (LLMs)—and supporting business decision-making by working with a modern data and AI/ML tech stack, including Python, GCP, AWS, Azure, LangChain, and both SQL and NoSQL databases like MongoDB.

Key Responsibilities

• Gather, clean, and preprocess structured and unstructured data from multiple sources.
• Analyze large datasets to identify trends, patterns, and actionable business insights.
• Develop, implement, and maintain machine learning and AI/LLM-based models (including prompt engineering and RAG/agent-based models) using frameworks such as LangChain, AutoGPT, CrewAI.
• Build data pipelines and manage data ingestion for scalable solutions across GCP, AWS, and Azure platforms.
• Work with both relational and NoSQL databases (example MongoDB) to store and manipulate data efficiently.
• Create automated scripts and tools in Python for data analysis, model evaluation, and deployment.
• Communicate findings through clear reports, dashboards, and data visualizations for technical and non-technical stakeholders.
• Collaborate with cross-functional teams, including data engineers, business analysts, and product managers, to propose and implement data-driven solutions.
• Stay updated with the latest advancements in AI, machine learning, and cloud technologies.

Required Skills & Qualifications

• Bachelor’s degree (or higher) in Computer Science, Engineering, Statistics, Mathematics, or related field.
• 1–3 years of practical experience as a Data Scientist or in a related data/AI role.
• Proficiency in Python (including libraries like NumPy, Pandas, Scikit-learn, and Matplotlib).
• Hands-on experience with LLMs, GenAI models, LangChain, and prompt engineering.
• Experience with cloud platforms: Google Cloud Platform (GCP), Amazon Web Services (AWS), and Microsoft Azure.
• Familiarity with data storage technologies, especially MongoDB (NoSQL) as well as SQL databases.
• Ability to design, build, and deploy end-to-end machine learning/data science solutions using modern MLOps practices.
• Strong analytical and problem-solving skills; ability to interpret and communicate complex findings.
• Excellent written and verbal communication skills; teamwork and collaboration abilities.

Preferred/Bonus Skills

• Experience with additional cloud-native AI/ML services and MLOps frameworks.
• Knowledge of APIs (e.g., Flask, FastAPI) for model deployment.
• Familiarity with other big data tools (Spark, Hadoop), and additional ML frameworks (TensorFlow, PyTorch) is advantageous