Data Scientist

Data Scientist – Job Description Location: Bangalore, India Experience: 5–6 Years Employment Type: Full-time We are looking for a Data Scientist with 5–6 years of experience who can work closely with our engineering and product teams to build data-driven intelligence capabilities for the platform. Key Responsibilities Data Science & Machine Learning

• Analyze large and complex datasets to identify patterns, trends, anomalies, and business opportunities.
• Develop statistical and machine-learning models for business and procurement use cases.
• Perform feature engineering, model selection, training, evaluation, and optimization.
• Develop models for forecasting, classification, clustering, anomaly detection, segmentation, and recommendation where applicable.
• Define appropriate evaluation metrics and validate model performance.
• Translate business problems into measurable data-science problems. Procurement & Business Intelligence Work on use cases such as:
• Spend analysis and classification
• Supplier performance and risk analysis
• Supplier segmentation
• Demand and inventory forecasting
• Price and cost analysis
• Procurement opportunity identification
• Anomaly and outlier detection
• Product/SKU-level analytics
• Channel and profitability analytics
• Business trend and market intelligence
• What-if and scenario analysis Generative AI / LLM
• Develop and integrate AI/LLM-powered capabilities
• Work with RAG (Retrieval-Augmented Generation) architectures.
• Develop embedding and semantic-search pipelines.
• Work with vector databases such as Pinecone, FAISS, or equivalent technologies .
• Experiment with LLMs and prompt engineering for enterprise use cases.
• Build AI workflows that combine LLMs with structured business data and application APIs.
• Evaluate the accuracy, relevance, and reliability of AI-generated results.
• Help establish approaches for reducing hallucinations and improving response quality. Data Engineering & Productionization
• Work closely with Data Engineers and Backend Engineers to build production-ready data pipelines.
• Work with structured and semi-structured data formats such as CSV, JSON and Parquet.
• Contribute to scalable ETL/ELT pipelines for datasets ranging from thousands to millions of records.
• Implement data validation, quality checks, transformations, and feature pipelines.
• Ensure models and analytical logic can be reproduced and deployed reliably.
• Collaborate on model deployment and monitoring in production environments. Collaboration
• Work closely with Product, Engineering, Data Engineering, and AI teams.
• Understand business requirements and convert them into technical/data-science solutions.
• Communicate analytical findings clearly to both technical and non-technical stakeholders.
• Participate in architecture and technical design discussions.
• Document models, assumptions, experiments, datasets, and results. Required Skills Core Data Science
• 5–6 years of hands-on experience in Data Science / Machine Learning.
• Strong Python programming skills.
• Strong understanding of statistics and probability.
• Experience with:
• Pandas
• NumPy
• Scikit-learn
• Matplotlib / Seaborn or equivalent visualization tools Strong understanding of supervised and unsupervised machine learning. Experience with model evaluation, feature engineering, and experimentation. Machine Learning Strong understanding of several of the following:
• Regression
• Classification
• Clustering
• Time-series forecasting
• Anomaly detection
• Recommendation systems
• NLP
• Dimensionality reduction
• Feature selection Generative AI Hands-on experience with:
• LLMs
• RAG
• Embeddings
• Vector databases
• Prompt engineering
• LangChain / LangGraph or equivalent frameworks
• OpenAI API or other LLM APIs Data
• Strong SQL skills.
• Experience working with large datasets.
• Understanding of data modeling and data quality.
• Experience with ETL/ELT concepts.
• Experience with cloud data platforms is desirable.
• Experience with Parquet, DuckDB, Spark, or similar technologies is a plus. Engineering
• Good understanding of REST APIs and service integration.
• Familiarity with Git and modern software development practices.
• Experience working with Docker and cloud environments is desirable.
• Exposure to CI/CD and MLOps practices is a plus. Good to Have Experience in one or more of the following areas would be highly valuable:
• Procurement analytics
• Supply chain analytics
• Retail analytics
• Inventory optimization
• Demand forecasting
• Spend analytics
• Supplier analytics
• SAP / SAP Ariba data
• Enterprise SaaS products
• Business intelligence platforms Education
• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.