DGM/GM/Sr GM - Date Scientist / Bengaluru

BVR People Consulting; Bengaluru; On-site; INR 1,500,000 - 2,600,000; Full time

Job summary

BVR People Consulting in Bengaluru is seeking a Data Scientist with hands-on ML and Generative AI experience to design, develop, and evaluate AI solutions for real-world business problems. You will translate business questions into data science plans, build classical ML and GenAI models, run experiments, and collaborate with software engineers and MLOps to deploy and monitor solutions in production.

The role emphasizes Python-based development, rigorous evaluation, strong documentation, and

Qualifications

• Python advanced proficiency and data analysis with NumPy/Pandas.
• Experience with scikit-learn, XGBoost/LightGBM, and statsmodels.
• Hands-on GenAI, prompt engineering, and RAG concepts.
• Ability to explain complex models to non-technical stakeholders.
• Experience delivering production-ready AI solutions.

Responsibilities

• Translate business problems into data science statements and solution approaches.
• Perform EDA, define success metrics, and evaluate models rigorously.
• Build and evaluate classical ML models (regression, classification, time-series).
• Develop GenAI solutions using LLMs, including retrieval-augmented generation.
• Collaborate with engineering/MLOps to productionize models and monitor performance.

Skills

Python

NumPy

Pandas

ML concepts

LLMs

Prompt engineering

Communication

Education

Bachelor's or Master's in DS/CS

Tools

scikit-learn

XGBoost

LightGBM

statsmodels

LangChain

Langgraph

Job description

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 summarisation 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 optimisation 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.
• Analyse model behaviour to detect overfitting, bias, hallucination, or performance degradation.
• Document assumptions, limitations, and recommendations for safe 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 modelling 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 cross‑functional 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.