𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐭𝐢𝐬𝐭 / 𝐌𝐋 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 – 𝐀𝐈 / 𝐌𝐋 𝐎𝐩𝐬

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.
• Strong 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).
• Strong problem-solving skills and ability to translate business problems into technical ML solutions.
• Excellent communication skills and experience presenting technical insights to diverse audiences.

Preferred Qualifications

• Published research or contributions in top-tier ML/AI conferences (e.g., NeurIPS, ICML, ACL, CVPR).
• Experience with large-scale distributed training, model compression, quantization, or inference optimization.
• Knowledge of MLOps tooling (e.g., MLflow, Weights & Biases, SageMaker, Vertex AI).
• Background in industrial applications (e.g., manufacturing, supply chain, energy, or finance).
• Hands-on experience with vector databases (e.g., Pinecone, Weaviate, Qdrant) and retrieval systems.
• Familiarity with MLOps best practices including model monitoring, drift detection, and retraining pipelines.
• Prior experience leading cross-functional AI/ML projects or mentoring junior data scientists.

Immediate joiners are highly preferred.

Skills: machine learning,a/b testing,airflow,rag,retrival system,mlops,genai,data science,ml models,kafka,aws,drift detection,llm,experiemental design,spark,sql,mlflow,prompt engineering,sagemaker,vector database,ai research,icml,generative ai,python,vertex ai,neurips