Description:

Develop and deploy NLP-driven solutions to extract actionable insights from

unstructured text data (Audit issues etc.,). Implement sentiment analysis, topic

modeling, and semantic clustering to transform raw text into structured, interpretable

outputs. Focus on enhancing decision-making through scalable, accurate, and

context-aware text analytics pipelines.

Skill Sets

NLP Expertise: Proficiency in libraries like spaCy, NLTK and Hugging Face Transformers

Text Processing: Tokenization, lemmatization, named entity recognition (NER), and

handling noisy/ambiguous text.

Modeling Techniques: Sentiment analysis (BERT, VADER, Text Rank), topic modeling

(LDA, NMF), and clustering (k-means, t-SNE).

Data Integration: Structuring outputs for downstream analysis using Pandas, Scikit-

learn.

Deployment Skills: Flask/Dash, cloud APIs, or MLOps tools for production-ready

pipelines.

Traits Expectation:

Analytical Precision: Attention to linguistic nuance and model accuracy in complex

datasets.

Innovative Adaptability: Creativity in customizing pre-trained models (e.g., GPT,

RoBERTa) for domain-specific tasks.

Collaborative Agility: Ability to align technical solutions with business goals (e.g.,

customer feedback analysis, market trend detection).

Expected learning:

Advanced NLP Techniques: Mastery of transformer-based models for sentiment

classification and contextual analysis.

Topic Modeling Proficiency: Identifying latent themes in large corpora using

probabilistic and neural methods.

Pipeline Development: Building scalable text processing workflows with deployment-

ready frameworks.

Real-World Application: Applying NLP to solve challenges like brand sentiment

tracking, document categorization, or crisis detection in social media.

Specific Prerequisites:

Coursera: Natural Language Processing Specialization (DeepLearning.AI).

DataCamp: Text Mining with Python.

Advanced Training:

Hugging Face: Transformers for NLP workshops.

fast.ai: Practical Deep Learning for Coders (focus on text tasks).

Tools & Platforms:

Proficiency in Jupyter, PyTorch/TensorFlow, and cloud NLP APIs.

Familiarity with Docker for deployment.

Hands-On Practice

Projects with real-world datasets (e.g., Twitter sentiment, legal document analysis).

Participation in Kaggle NLP competitions or GitHub open-source contributions.

Standard Chartered - Data Science

Station Overview

Project 1

Title: Text-to-Insight: Advanced NLP Analytics for Strategic Decision-Making in Audits

Project 2

Title: Predictive Modeling and Data Insights