Role: Data Scientist / ML Researcher – AI / ML Ops

Experience: 6–8 Years

Location: Mumbai / Ahmedabad

Core Domains: Advanced Analytics, Experimentation, & Generative AI

Core Responsibilities & Technical Tracks

Algorithm Development & Statistical Modeling

Creating and optimizing predictive models using advanced statistical frameworks. Implementing classical and deep learning ML algorithms tailored to industrial use cases.

Feature Engineering & Experimentation

Designing complex feature engineering pipelines to maximize signal extraction from diverse, raw datasets. Managing rigorous scientific experimentation—including reproducible research, parameter logging, and experiment tracking.

GenAI Use Cases & Evaluation

R&D; and prototyping for cutting-edge GenAI applications, including LLMs, fine-tuning workflows, and RAG architectures. Defining robust evaluation criteria to assess model robustness, drift, bias, and business readiness before deployment into production MLOps pipelines.

Business Translation & Communication





Translating ambiguous business problems into well-defined ML deliverables. Communicating technical results clearly to both technical and non-technical stakeholders.

Required Qualifications

Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, or a related field; Master’s or PhD preferred.
6–8 years of hands-on experience in data science, machine learning, or AI research.
Solid proficiency in Python (including libraries such as scikit-learn, TensorFlow, PyTorch, LangChain, Hugging Face Transformers).
Experience building, deploying, and monitoring ML models in production environments (MLOps, AWS).
Solid understanding of statistical modeling, hypothesis testing, experimental design (e.g., A/B testing), and evaluation metrics.
Demonstrated experience with LLMs, fine-tuning, RAG, prompt engineering, and GenAI application development.
Familiarity with data engineering concepts and tools (e.g., Spark, SQL, Airflow, Kafka).
Stron

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