IN_Senior Associate_Data Scientist_Data and Analytics_Advisory_Bangalore

• Work with structured, semi‑structured, and unstructured data 

• Perform data cleaning, transformation, feature engineering, and integration 

• Build and validate statistical and machine learning models 

• Apply techniques such as:  

• Regression, Classification, Clustering 

• Time Series Forecasting 

• Design and analyze experiments (A/B testing, hypothesis testing) 

• Write clean, modular, production‑quality code 

• Follow best practices (Git, code reviews, documentation) 

• Communicate insights through presentations, reports, and dashboards 

• Partner with business, engineering, and product stakeholders 

Specialization Tracks (Any One or More) 

1. Data Science + MLOps 

• Focus on productionizing and scaling ML models 

• Deploy models using Docker, APIs (Flask/FastAPI), or cloud services 

• Build and maintain ML pipelines 

• Implement model monitoring, performance tracking, and retraining strategies 

• Work with CI/CD, Airflow, Databricks, or cloud platforms (AWS/Azure/GCP) 

• Ensure model reliability, scalability, and governance 

2. Data Science + Agentic AI ( Mandatory )  

• Focus on LLM‑powered and autonomous AI systems 

• Build Agentic AI workflows using LLMs 

• Develop reasoning, planning, tool‑using AI agents 

• Integrate LLMs with enterprise data and APIs 

• Apply prompt engineering, RAG, and multi‑agent orchestration 

• Evaluate and improve AI responses, safety, and efficiency 

3. Data Science + Power BI / Tableau 

• Focus on analytics, visualization, and business intelligence 

• Design interactive dashboards using Power BI / Tableau 

• Translate analytical models into decision‑ready visuals 

• Partner with stakeholders to define KPIs and metrics 

• Automate reporting and analytics workflows 

• Enable self‑service analytics for business teams 

4. Data Science + UI/UX 

• Focus on data‑driven product and user experience design 

• Translate complex models into simple, intuitive user interfaces 

• Collaborate with UI/UX designers and front-end teams 

• Design data‑heavy applications, dashboards, and workflows 

• Apply user research and usability principles to analytics products 

• Improve adoption of ML solutions through better UX