ROLE OVERVIEW

We are looking for a Data Scientist with 5+ years of hands-on experience in Machine Learning, Deep Learning, advanced analytics, and AI solution development. The ideal candidate should have robust expertise in building supervised, unsupervised, and deep learning models, with practical experience across model experimentation, feature engineering, neural network development, model evaluation, deployment, and monitoring. The candidate should also have strong working knowledge of Microsoft Azure and Databricks, with exposure to Generative AI and Agentic AI use cases.

KEY RESPONSIBILITIES

- Design, develop, validate, and deploy Machine Learning models for classification, regression, forecasting, recommendation, anomaly detection, optimization, NLP, and predictive analytics use cases.
- Build Deep Learning solutions using neural networks, CNNs, RNNs, LSTMs, transformers, autoencoders, and sequence modelling approaches where applicable.
- Perform data exploration, statistical analysis, feature engineering, feature selection, model tuning, cross-validation, hyperparameter optimization, and error analysis.
- Develop scalable model training and inference workflows using Azure Machine Learning, Databricks, Spark, MLflow, and production-grade MLOps practices.
- Work with structured, semi-structured, and unstructured data from enterprise data platforms, data lakes, APIs, documents, images, text, and time-series sources.
- Apply NLP and text analytics techniques including tokenization, embeddings, semantic search, text classification, entity extraction, summarization, and sentiment analysis.
- Contribute to Generative AI and Agentic AI solutions using LLMs, prompt engineering, retrieval-augmented generation, embeddings, vector search, orchestration, and evaluation frameworks.
- Collaborate with business stakeholders, data engineers, architects, and product teams to convert business problems into measurable ML, Deep Learning, and AI solutions.
- Ensure models are interpretable, reliable, governed, monitored, and aligned with responsible AI, data security, performance, and scalability standards.

REQUIRED EXPERIENCE

- 5+ years of experience in Data Science, Machine Learning, Deep Learning, AI engineering, advanced analytics, or related roles.




- Strong hands-on experience building ML models using Python and libraries such as Scikit-learn, XGBoost, LightGBM, Pandas, NumPy, SciPy, Statsmodels, TensorFlow, PyTorch, Keras, and MLflow.
- Hands-on experience with Deep Learning architectures including feed-forward neural networks, CNNs, RNNs, LSTMs, transformers, autoencoders, attention mechanisms, and transfer learning.
- Strong understanding of model development lifecycle including data preparation, feature engineering, training, validation, tuning, testing, deployment, monitoring, drift detection, and retraining.
- Strong hands-on experience with Microsoft Azure cloud ecosystem for data science, ML engineering, model deployment, and AI workloads.
- Strong hands-on experience with Databricks for large-scale data processing, feature engineering, experimentation, model development, distributed training, and pipeline execution.
- Experience working with large-scale data using Spark, Delta Lake, SQL, PySpark, distributed processing, and lakehouse patterns.
- Exposure to Generative AI, LLM applications, prompt engineering, RAG architecture, vector databases, and AI evaluation frameworks.
- Understanding of Agentic AI concepts such as AI agents, orchestration, memory, tool calling, workflow automation, and human-in-the-loop review.
- Ability to communicate ML concepts, model results, trade-offs, limitations, and recommendations clearly to business and technology stakeholders.

TECHNICAL SKILLS

Skill Area

Expected Expertise

Machine Learning

Classification, regression, clustering, forecasting, recommendation systems, anomaly detection, optimization, ensemble models, feature engineering, model evaluation, explainability

Deep Learning

Neural networks, CNNs, RNNs, LSTMs, transformers, autoencoders, attention mechanisms, transfer learning, embeddings, model fine-tuning, GPU-based training concepts

NLP and Text Analytics

Text classification, semantic search, entity extraction, summarization, sentiment analysis, embeddings,



transformer-based NLP models, document intelligence use cases

Cloud Platform

Azure Machine Learning, Azure OpenAI, Azure Data Factory, Azure Synapse, Azure Data Lake, Azure Functions, Azure DevOps, model endpoints, deployment pipelines

Databricks

Databricks notebooks, Jobs, Workflows, Delta Lake, Spark, PySpark, MLflow, Unity Catalog, feature engineering, distributed model training, model lifecycle management

Generative AI

LLMs, prompt engineering, RAG, embeddings, vector search, AI application design, model evaluation, responsible AI practices

Agentic AI

Agent workflows, task planning, tool calling, orchestration frameworks, autonomous assistants, multi-step reasoning patterns

Programming and MLOps

Python, SQL, PySpark, REST APIs, Git, CI/CD, MLflow, experiment tracking, model registry, monitoring, drift detection, reproducible ML pipelines

Visualization

Power BI, dashboards, storytelling with data, KPI reporting, stakeholder-ready insights, model performance reporting

PREFERRED QUALIFICATIONS

- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, Artificial Intelligence, Machine Learning, or a related discipline.
- Relevant certifications in Azure, Databricks, Data Science, Machine Learning, Deep Learning, MLOps, or Generative AI are preferred.
- Experience with enterprise-scale ML solution design, deep learning model development, model governance, MLOps, and production support.
- Exposure to solution accelerators, reusable ML components, feature stores, model evaluation frameworks, prompt libraries, or agent frameworks.
- Ability to work in agile delivery environments and contribute to estimation, planning, demos, documentation, experimentation reports, and stakeholder reviews.

EXPECTED OUTCOMES

- Deliver scalable, secure, and production-ready Machine Learning and Deep Learning solutions on Azure and Databricks.
- Improve business decision-making through predictive models, advanced analytics, AI assistants, automation, and data-driven insights.
- Build reusable ML assets, feature engineering patterns, model evaluation frameworks, and deployment-ready AI components.
- Support innovation across Machine Learning, Deep Learning, Generative AI, and Agentic AI use cases.

📌 Sr Data Scientist (Mumbai)
🏢 Exponentia.ai
📍 Mumbai