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.