Data Scientist

Data AI/ML Scientist

Location:

Experience:

Education:

Role Overview

AI/ML Scientist

Key Responsibilities

• Design, build, and deploy models across

  classical ML, statistical reasoning, deep learning, NLP, CV, GenAI, and Agentic AI

  .
• Build

  statistical reasoning models

  for forecasting, inference, and optimization.
• Conduct rigorous

  experimentation, validation, and A/B testing

  .
• Develop

  production-ready ML pipelines

  with engineering teams.
• Research and integrate

  latest AI/ML advancements

  into enterprise solutions.

Skills & Tools

• Classical ML:

  Regression, Classification, Trees, Ensembles, XGBoost.

• Statistics:

  Hypothesis testing, Bayesian inference, Time Series forecasting, Causal modeling.

• Deep Learning:

  CNNs, RNNs, Transformers, GANs, Reinforcement Learning.

• Generative AI & LLMs:

  Prompt Engineering, Fine-tuning, Hugging Face, LangChain.
• Tools:

  Python, R, TensorFlow, PyTorch, Scikit-learn, Statsmodels

  .

• MLOps:

  MLflow, Kubeflow, Vertex AI, SageMaker, Databricks ML.

Certifications (Preferred)

• Cloud AI/ML certifications (AWS ML Specialty, GCP ML Engineer, Azure AI)

• Deep Learning Specializations (Stanford, Coursera, Databricks ML Certs)

Soft Skills

• Ability to explain complex ML models in simple business terms.
• Strong data storytelling and presentation skills.
• Curious, innovative, and research-driven mindset.
• Leadership to mentor teams of ML engineers & data scientists.