Senior Data Scientist

sugar.fit; Bengaluru; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

sugar.fit is seeking an experienced Senior Data Scientist to lead predictive, prescriptive, and generative AI initiatives. You will bridge business strategy with advanced technical execution, extracting insights from large datasets and scaling models into production.

You will own ML solutions end-to-end, mentor teammates, present findings to executives, and shape data governance, experimentation, and model validation frameworks across the organization.

Qualifications

• 5-8+ years of professional data science/ML experience.
• Proven deployment of enterprise ML models to production.
• Strong SQL and data warehouse experience.

Responsibilities

• Design and deploy advanced ML algorithms, models, and time-series forecasts.
• Implement Generative AI strategies including fine-tuning LLMs and Agentic AI workflows.
• Own the MLOps lifecycle with automated retraining, model registries, and inference strategies.
• Extract and clean large datasets using structured SQL across cloud data warehouses.
• Establish validation frameworks for data quality, schema adherence, and feature engineering.
• Standardise experimentation by building modular data models and managing A/B testing.
• Translate ambiguous business challenges into technical specs and analytic frameworks.
• Mentor junior team members with code reviews and guidance.
• Present findings and data stories to executives and VP/C-level leaders.

Skills

Python

ML libraries

Deep learning

SQL

Apache Spark

LLMs

RAG architectures

Prompt orchestration

Cloud & DevOps

Mathematics

Mentoring

Executive storytelling

Education

Bachelor's degree in CS/DS

Master's degree

PhD (preferred)

Tools

Git

CI/CD

AWS

GCP/Azure

Job description

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.

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.
• Own the MLOps lifecycle, managing automated retraining pipelines, model registries, and inference strategies.
• 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.
• 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.

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 professional experience in data science, analytics, or machine learning engineering.
• Track Record: Proven success in deploying multiple enterprise-grade ML models into live production systems.