Data Scientist

Smart Soource Pune; Pune District; Hybrid; INR 1,500,000 - 2,500,000; Full time

Job summary

Smart Source Pune is seeking a Data Scientist to join a dynamic AI team in Pune. You will prototype GenAI-based solutions and MVPs, working on ML models, LLMs, and chatbots to drive enterprise AI initiatives.

The role emphasizes hands‑on ML development, data handling, and collaboration with AI Engineers, with a focus on pragmatic, scalable AI solutions in a fast-paced environment.

Qualifications

• 4–7 years of experience in data science, ML, or AI roles.
• Hands‑on experience with ML model development and deployment.
• Exposure to GenAI technologies, including LLMs and chatbots.

Responsibilities

• Execute PoCs and MVPs for AI/ML projects with practical implementations.
• Develop and refine ML models using supervised and unsupervised learning.
• Build and deploy GenAI solutions (LLMs, RAG systems, chatbots).
• Collaborate with AI Engineers to integrate models into enterprise apps.
• Collect, preprocess, and explore data to enable model training.
• Evaluate models, monitor GenAI outputs, and iterate for improvement.
• Document processes and contribute to AI/ML playbooks.

Education

Bachelors or Masters in Computer Science, Data Science, AI, or a related field

Tools

Python

SQL

scikit-learn

TensorFlow

PyTorch

Matplotlib

Seaborn

MLflow

Airflow

Azure

AWS

GCP

Job description

About the Role:

We are looking for a motivated Data Scientist to join our dynamic AI team in Pune. This role will focus on proving concepts through rapid prototypes and building Minimum Viable Products (MVPs) in context of machine learning (ML) and Generative AI (GenAI) solutions, including GenAI-based chatbots. The ideal candidate will have a strong foundation in data science, with hands‑on experience in ML models, familiarity with GenAI based solutions, and a keen interest in emerging AI technologies.

Key Responsibilities:

AI & ML Model Development:

• Execute PoCs and MVPs for AI and ML projects, focusing on practical implementation.
• Develop and refine ML models, including supervised and unsupervised learning algorithms.
• Build and deploy GenAI solutions, such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and chatbots.
• Collaborate with AI Engineers to integrate models into enterprise applications.

Data Handling & Preprocessing:

• Collect, clean, and preprocess data to ensure quality inputs for model training.
• Conduct exploratory data analysis (EDA) to uncover insights and inform model development.
• Work with Data Engineers to ensure smooth data pipelines and infrastructure.

Model Evaluation & Optimization:

• Evaluate model performance using appropriate metrics and fine-tune algorithms for optimal results.
• Monitor GenAI outputs for issues such as bias, hallucinations, and accuracy.
• Implement feedback loops for continuous model improvement.

Collaboration & Documentation:

• Work closely with the AI Lead and cross-functional teams to align projects with business objectives.
• Document model development processes, code, and findings for transparency and reproducibility.
• Contribute to the development of best practices and playbooks for AI and ML solutions.

Continuous Learning & Development:

• Stay updated with the latest trends and advancements in AI, ML, and GenAI technologies.
• Participate in team knowledge‑sharing sessions and training opportunities.

Qualifications & Skills:

Education:

• Bachelors or Masters degree in Computer Science, Data Science, AI, or a related field.

Experience:

• 4-7 years of experience in data science, machine learning, or AI-related roles.
• Hands‑on experience with ML model development and deployment.
• Exposure to GenAI technologies, including LLMs and chatbot development, is a plus.

Technical Skills:

• Proficient in Python and SQL for data manipulation and model development.
• Familiarity with ML frameworks and libraries such as scikit‑learn, TensorFlow, or PyTorch.
• Experience with data visualization tools (e.g., Matplotlib, Seaborn) and EDA techniques.
• Basic understanding of MLOps tools like MLflow, Airflow, or similar is an advantage.
• Exposure to cloud platforms (Azure, AWS, GCP) for AI/ML model deployment is a plus.

Soft Skills:

• Strong analytical and problem‑solving skills.
• Eagerness to learn and adapt in a fast‑paced, evolving environment.
• Detail-oriented with a focus on delivering high‑quality work.