Role Overview

We are looking for a Data Scientist with strong hands-on experience in Classical Machine Learning and Generative AI to design, develop, and evaluate AI solutions for real-world business problems.

This role focuses on data-driven problem solving, model development, experimentation, and evaluation, working closely with engineering and MLOps teams to operationalize solutions. The emphasis is on statistical rigor, ML modeling, and AI reasoning.

The ideal candidate has a solid foundation in Python-based ML development, applied GenAI use cases, and experience delivering production-ready AI solutions in enterprise environments.

Key Responsibilities:

Business Problem Framing & Data Analysis

- Translate business problems into clear data science problem statements and solution approaches.
- Perform exploratory data analysis (EDA) to identify patterns, data quality issues, and feature opportunities.
- Define success metrics and evaluation criteria aligned with business outcomes.

Classical Machine Learning Development

- Build, train, and evaluate classical ML models, including:

- Regression and classification models

- Time series forecasting

- Clustering and segmentation

- Anomaly detection
- Perform feature engineering, preprocessing, and model selection to improve performance and robustness.
- Apply statistical techniques to validate results and ensure model stability.

Generative AI & LLM-Based Solutions

- Develop Generative AI solutions using Large Language Models for use cases such as:
- Knowledge assistance and Q&A;

- Text summarization and extraction

- Reasoning and decision support
- Design and implement retrieval-augmented generation (RAG) workflows, including document processing and retrieval logic.
- Perform prompt engineering, testing, and optimization to improve output quality and consistency.
- Contribute to agent-oriented AI solution design from a reasoning and orchestration perspective (not infrastructure).

Model Evaluation & Quality Assurance





- Design and execute evaluation frameworks for ML and GenAI solutions, including offline tests and validation datasets.
- Analyze model behavior to detect overfitting, bias, hallucination, or performance degradation.
- Document assumptions, limitations, and recommendations for protected production use.

Collaboration & Production Readiness

- Collaborate with wider team to transition models from development to production.
- Support model handover, documentation, and knowledge transfer.
- Handle monitoring signals, retraining needs, and lifecycle management.

Learning, Reuse & Best Practices

- Stay up to date with advancements in ML and Generative AI and assess relevance for enterprise use cases.
- Contribute to reusable modules, feature libraries, and solution templates.
- Share learnings through reviews, demos, and internal knowledge forums.

Required Skills: Programming & Data Science

- Python (advanced proficiency)
- Data analysis and modeling using NumPy, Pandas

Classical Machine Learning

- Experience with popular ML libraries such as:
- scikit-learn

- XGBoost / LightGBM
- statsmodels
- Strong understanding of supervised and unsupervised learning techniques
- Feature engineering, model tuning, and evaluation

Generative AI

- Hands-on experience with:

- Large Language Models (LLMs)

- Prompt engineering

- RAG concepts
- Familiarity with GenAI libraries and ecosystems such as:

- LangChain

- Langgraph
- Understanding of strengths and limitations of GenAI vs classical ML

Problem Solving & Communication

- Strong analytical and critical thinking skills
- Ability to explain complex models and results to nontechnical stakeholders
- Structured approach to experimentation and documentation

Preferred Qualifications

- Experience delivering endtoend AI solutions from experimentation to production.
- Exposure to responsible AI practices, including explainability, fairness, and validation.
- Experience working in crossfunctional teams with engineering and product.
- Prior mentoring of junior data scientists is a plus.

Education

- Bachelors or Master’s degree in Data Science, Computer Science, Statistics, AI/ML or related field.

📌 DGM/GM/Sr GM - Date Scientist / Bengaluru
🏢 BVR People Consulting
📍 Bengaluru