Data Science Analyst

Role Overview As a Data Science Analyst at Jai Kisan, you will play a critical role in transforming data into actionable insights to inform strategic business decisions. You’ll work cross-functionally with product, engineering, operations, and leadership teams to unlock the full potential of data through advanced analytics, automation, and AI-driven insights. This role requires a solid foundation in data handling, modern analytics tooling, and a deep curiosity for leveraging emerging technologies like LLMs, vector databases, and cloud-native platforms. Key Responsibilities Collect, clean, preprocess, and validate datasets from diverse structured and unstructured sources including APIs, data lakes, and real-time streams. Conduct exploratory data analysis (EDA) to identify trends, correlations, and business opportunities using statistical and machine learning techniques. Build, maintain, and optimize scalable data pipelines using Airflow, dbt, or Dagster to support both batch and real-time analytics. Develop and deploy AI/ML models, including LLM-based applications, for predictive analytics, recommendation systems, and automation use cases. Work with vector databases (e.g., Pinecone, Weaviate, Chroma) for semantic search and embedding-based applications. Design and manage dashboards and self-serve analytics tools using Power BI, Looker Studio, or Tableau to enable data-driven decisions. Collaborate with backend and data engineers to integrate data solutions into microservices and APIs. Interpret and clearly communicate complex analytical findings to stakeholders, including non-technical teams. Stay ahead of industry trends including AI advancements, data governance, MLOps, vector search, and cloud-native services. Required Skills & Technologies Databases: Proficient in SQL, PostgreSQL, MongoDB, with working knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate). Languages & Tools: Strong programming experience in Python (pandas, NumPy, scikit-learn, LangChain, PyTorch/TensorFlow), SQL, and optionally R. Data Workflow Tools: Experience with Apache Airflow, dbt, Dagster, or similar tools. BI & Visualization: Proficiency in Power BI, Tableau, Looker Studio, Plotly, or Matplotlib. AI/ML: Exposure to LLMs (GPT, BERT, etc.), embedding models, and AI prompt engineering for analytics augmentation. Data APIs & Embeddings: Familiarity with OpenAI, Cohere, Hugging Face APIs for vector search and semantic understanding. Cloud Platforms: Hands-on experience with AWS, GCP, or Azure, especially with services like S3, BigQuery, Redshift, Athena, or Azure Synapse. Version Control & DevOps: Experience using Git, CI/CD pipelines, and Docker is a plus. Qualifications Bachelor's or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Economics, or a related field. 1–3 years of hands-on experience in a data analysis or applied machine learning role. Strong problem-solving and storytelling abilities with a deep sense of ownership. Excellent communication and collaboration skills; ability to translate technical findings into business impact. Good To Have Experience in MCP (Multi-Cloud Platforms) and cloud-agnostic data pipelines. Understanding of data mesh, data fabric, or modern data stack architectures. Contributions to open-source analytics tools or AI projects. Knowledge of data privacy, compliance standards (GDPR, SOC2), and data security best practices. Show more Show less