Data Scientist

Job DescriptionAbout CoffeeBeans Consulting

CoffeeBeans is a tech-driven software consulting company that helps businesses solve complex problems using modern data, AI, and engineering solutions. We blend deep technical expertise with a product mindset to build scalable, intelligent, and high-impact solutions across industries. Our data science team works on end-to-end delivery—from exploration and modeling to GenAI application development and deployment.

Location : Bangalore / Pune (with short/medium-term travel opportunities within India & foreign location)

Work mode : Hybrid( 3 days WFO)Role Overview

As an L2 Data Scientist, you will play a hands-on role in delivering production-grade ML and GenAI-powered applications. You are expected to independently take ownership of data science components within client projects, contribute to solutioning and design, and mentor junior team members. You will work across a range of use cases such as personalization, fraud detection, intelligent automation, RAG pipelines, and LLM-based assistants.

This role is ideal for someone who has proven experience in both traditional ML and an emerging understanding of LLMs and generative AI applications.

Key ResponsibilitiesML & Data Science

Own and deliver ML model development, tuning, and evaluation for client-facing projects.

Lead exploratory data analysis, data preprocessing, and feature engineering with minimal supervision.

Build models using appropriate ML techniques (classification, regression, clustering, recommendation) and ensure performance meets business expectations.

Contribute to experimentation frameworks and model reproducibility best practices.

GenAI & LLM Applications

Design and prototype GenAI solutions using LLMs (e.g., OpenAI, Claude, Mistral, Llama).

Build RAG pipelines, prompt templates, few-shot learning prompts, and evaluation mechanisms for GenAI systems.

Integrate LLMs with APIs, vector databases (e.g., Pinecone, FAISS, Weaviate), and context providers.

Contribute to benchmarking, safety, and cost-performance trade-offs in LLM app development.

Product & Engineering Collaboration

Collaborate with engineering teams to take models from experimentation to deployment (batch/real-time).

Assist in building APIs and data pipelines needed for productionizing models.

Contribute to technical documentation, explainability reports, and client presentations.

Team & Growth

Mentor junior data scientists and review code/model design.

Stay current with advances in ML and GenAI to inform solution design and share knowledge internally.

Participate in discovery and solutioning phases with clients alongside tech leads and PMs.

Required Skills & Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Statistics, or a related field.

years of hands-on experience in applied data science, including both ML model development and GenAI-based solutioning.

Strong command over Python and ML libraries (scikit-learn, XGBoost, LightGBM, etc.).

Experience working with LLM APIs (OpenAI, Cohere, Claude, etc.) and frameworks (LangChain, LlamaIndex, or similar).

Hands-on experience with prompt engineering, RAG workflows, and evaluating LLM outputs.

Proficiency in SQL and data wrangling tools (pandas, NumPy).

Experience working with REST APIs, Git, and cloud environments (AWS/GCP).

Good-to-Have Skills

Experience with deploying models via FastAPI, Docker, or serverless platforms.

Knowledge of MLOps tools (MLflow, DVC) and monitoring frameworks.

Experience with embeddings, vector databases, and similarity search.

What You Can Expect at CoffeeBeans

Work on real-world AI/ML problems across verticals.

Be part of a fast-moving team delivering end-to-end ML & GenAI apps.

Collaborate with experienced engineers and PMs in a flat and open culture.

Opportunities to lead, mentor, and influence tech direction.

Job Types: Full-time, Permanent

Pay: Up to ₹2,400,000.00 per year

Application Question(s):

• Last Working Date?

Experience:

• Total Work: 5 years (Preferred)

Work Location: In person