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Data Scientist - L2
Data Scientist - L2
CoffeeBeans 's logo

Data Scientist - L2

Nikita Sinha's profile picture
Posted by Nikita Sinha
3 - 5 yrs
Upto ₹23L / yr (Varies
)
Bengaluru (Bangalore), Pune
Skills
Artificial Intelligence (AI)
skill iconMachine Learning (ML)
skill iconPython
Large Language Models (LLM)
Natural Language Processing (NLP)
SQL

As an L1/L2 Data Scientist, you’ll work alongside experienced engineers and data scientists to solve real-world problems using machine learning (ML) and generative AI (GenAI). Beyond classical data science tasks, you’ll contribute to building and fine-tuning large language model (LLM)– based applications, such as chatbots, copilots, and automation workflows.


Key Responsibilities

  • Collaborate with business stakeholders to translate problem statements into data science tasks.
  • Perform data collection, cleaning, feature engineering, and exploratory data analysis (EDA).
  • Build and evaluate ML models using Python and libraries such as scikit-learn and XGBoost.
  • Support the development of LLM-powered workflows like RAG (Retrieval-Augmented Generation), prompt engineering, and fine-tuning for use cases including summarization, Q&A, and task automation.
  • Contribute to GenAI application development using frameworks like LangChain, OpenAI APIs, or similar ecosystems.
  • Work with engineers to integrate models into applications, build/test APIs, and monitor performance post-deployment.
  • Maintain reproducible notebooks, pipelines, and documentation for ML and LLM experiments.
  • Stay updated on advancements in ML, NLP, and GenAI, and share insights with the team.

Required Skills & Qualifications

  • Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
  • 2.5–5 years of experience in data science, ML, or AI (projects and internships included).
  • Proficiency in Python with experience in libraries like pandas, NumPy, scikit-learn, and matplotlib.
  • Basic exposure to LLMs (e.g., OpenAI, Cohere, Mistral, Hugging Face) or strong interest with the ability to learn quickly.
  • Familiarity with SQL and structured data handling.
  • Understanding of NLP fundamentals and vector-based retrieval techniques (a plus).
  • Strong communication, problem-solving skills, and a proactive attitude.

Nice-to-Have (Not Mandatory)

  • Experience with GenAI prototyping using LangChain, LlamaIndex, or similar frameworks.
  • Knowledge of REST APIs and model integration into backend systems.
  • Familiarity with cloud platforms (AWS/GCP/Azure), Docker, or Git.
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I had an amazing experience. It was a delight getting interviewed via Cutshort. The entire end to end process was amazing. I would like to mention Reshika, she was just amazing wrt guiding me through the process. Thank you team.
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About CoffeeBeans

Founded :
2017
Type :
Products & Services
Size :
20-100
Stage :
Bootstrapped

About

CoffeeBeans Consulting is a technology partner dedicated to driving business transformation. With deep expertise in Cloud, Data, MLOPs, AI, Infrastructure services, Application modernization services, Blockchain, and Big Data, we help organizations tackle complex challenges and seize growth opportunities in today’s fast-paced digital landscape. We’re more than just a tech service provider; we're a catalyst for meaningful change

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Tech stack

skill iconJava
SQL
NOSQL Databases

Candid answers by the company

What does the company do?
What is the location preference of jobs?

CoffeeBeans Consulting, founded in 2017, is a high-end technology consulting firm that helps businesses build better products and improve delivery quality through a mix of engineering, product, and process expertise. They work across domains to deliver scalable backend systems, data engineering pipelines, and AI-driven solutions, often using modern stacks like Java, Spring Boot, Python, Spark, Snowflake, Azure, and AWS. With a strong focus on clean architecture, performance optimization, and practical problem-solving, CoffeeBeans partners with clients for both internal and external projects—driving meaningful business outcomes through tech excellence.

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