? Role Overview

We are seeking an experienced and innovative Senior Data Scientist to lead the development of predictive, prescriptive, and generative AI models. In this role, you will act as a critical bridge between business strategy and advanced technical execution. You will extract actionable insights from large, complex datasets and scale machine learning models directly into production environments to solve real-world problems.

? Key Responsibilities

Technical Execution & Modeling

- Design and deploy advanced machine learning algorithms, statistical models, and time-series forecasts.
- Implement Generative AI strategies, including fine-tuning Large Language Models (LLMs) and building Agentic AI workflows.
- Write production-grade Python code utilizing distributed computing frameworks.
- Own the MLOps lifecycle, managing automated retraining pipelines, model registries, and inference strategies.

Data Architecture & Quality

- Extract and clean massive datasets using structured SQL queries across cloud data warehouses.
- Establish validation frameworks to ensure high data quality, strict schema adherence, and accurate feature engineering.
- Standardise experimentation by building modular data models and managing the full lifecycle of A/B testing.

Leadership & Strategy





- Translate ambiguous business challenges into technical specifications and structured analytical frameworks.
- Mentor junior team members, providing code reviews, technical guidance, and career development support.
- Present complex findings and data stories to non-technical executive stakeholders and VP/C-level leaders.

?️ Required Skills & Qualifications

Technical Profile

- Programming: Mastery of Python, standard ML libraries (Scikit-Learn, XGBoost), and deep learning frameworks (PyTorch, TensorFlow).
- Big Data: Proficiency with SQL and distributed frameworks like Apache Spark.
- GenAI Stack: Deep understanding of LLMs, RAG architectures, vector databases, and prompt orchestration tools.
- Cloud & DevOps: Experience with cloud ecosystems (AWS, GCP, or Azure) alongside Git and CI/CD pipelines.
- Mathematics: Solid foundation in predictive statistics, experimental design, calculus, and linear algebra.

Professional Experience

- Education: Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Statistics, Mathematics, or a related quantitative discipline.
- Experience: Minimum 5 to 8+ years of qualified experience in data science, analytics, or machine learning engineering.
- Track Record: Proven success in deploying multiple enterprise-grade ML models into live production systems.

📌 Senior Data Scientist (Bengaluru)
🏢 sugar.fit
📍 Bengaluru