Data Scientist

Wenger & Watson; Gurugram District, Bengaluru, Mumbai; Hybrid; INR 1,339,000 - 2,009,000; Full time

Job summary

Wenger & Watson is seeking an experienced Data Scientist focused on Gen AI and NLP to drive advanced text analytics. The role requires a strong ML background, statistical analysis, and a track record of extracting insights from large textual datasets.

You will develop NLP models, preprocess massive text corpora, engineer features, and collaborate with cross-functional teams to deploy scalable solutions on cloud platforms.

Qualifications

• Master's in Computer Science, Data Science, Statistics, or a related field.

Responsibilities

• Develop and implement NLP models for text classification, sentiment analysis, entity recognition, and translation.
• Preprocess and clean large text datasets using tokenization, stemming, and lemmatization.
• Identify and engineer features from text data to improve model performance.
• Train, fine-tune, and evaluate NLP models using suitable algorithms and metrics.
• Conduct exploratory data analysis on textual datasets to uncover patterns and insights.
• Collaborate with data engineers and software developers to integrate NLP solutions into systems.
• Optimize NLP algorithms for efficiency and accuracy, including parallel processing.

Skills

Python

R

NLP libraries

ML modeling

Text mining

Statistical analysis

Data visualization

Education

Master's in Computer Science/Data Science/Statistics

Tools

NLTK

SpaCy

TensorFlow

PyTorch

Job description

Job Summary:We are seeking an experienced Data Scientist with a focus onGen AIand a minimum of5years of industry experience. The ideal candidate will have a strong background in machine learning, statistical analysis, and data modeling, witha specificemphasis on extracting insights from textual data. This role involves working on complex NLP projects to derive meaningful patterns and knowledge from large datasets.
Key Responsibilities:

NLP ModelDevelopment:

• Develop and implement NLP models for various applications, including text classification, sentiment analysis, entity recognition, and language translation.

-Utilizemachine learning and deep learning techniques to extract valuable information from unstructured text.

TextPreprocessing:

• Preprocess and clean large volumes of text data to prepare it for analysis.
• Implement techniques such as tokenization, stemming, and lemmatization.

FeatureEngineering:

-Identifyand engineer relevant features from text data to improve model performance.

• Implement techniques to capture semantic relationships within the data.

Model Training andEvaluation:

• Train, fine-tune, andvalidateNLP models usingappropriate algorithms.
• Evaluate model performance using metrics such as precision, recall, F1 score, and accuracy.

DataAnalysis:

• Conduct exploratory data analysis on textual datasets to uncover patterns, trends, and anomalies.
• Use statistical methods to gaininsightsinto the underlying structure of the data.

Collaboration with Cross-FunctionalTeams:

• Work closely with data engineers, software developers, and domain experts to integrate NLP solutions into broader systems and applications.
• Collaborate in defining project goals, requirements, and deliverables.

AlgorithmOptimization:

• Optimize and fine-tune NLP algorithms for improved efficiency and accuracy.
• Implement parallel processing anddistributedcomputing techniques for large-scale data analysis.

Stay Updated with NLPTrends:

• Stay informed about the latest advancements and trends in NLP and machine learning.
• Applycutting-edgetechniques to solve complex business problems.

Documentation:

• Document methodologies, algorithms, and code for reproducibility and knowledge transfer.
• Prepare clear and concise project reports and documentation.

Qualifications:

• -Master's in Computer Science, Data Science, Statistics, ora related field.

• Minimum of3years of professional experience in data science, with a focus onGenAi.
• Strong programming skills in languages such as Python or R.

-Expertisein using NLP libraries and frameworks such as NLTK,SpaCy, or TensorFlow.-Proficiencyin machine learning techniques, including supervised and unsupervised learning.

• Experience with deep learning frameworks like TensorFlow orPyTorch.
• Strong understanding of text mining, information retrieval, and semantic analysis.
• Excellent problem-solving and analytical skills.
• Effective communication and collaboration skills.
• Experience with cloud platforms (e.g., AWS, Azure) and big data technologies is a plus