Person will be responsible for working on Data Science related problems involving un-structured (primarily) as well as structured data. The role requires independent solution design and its implementation into production level Analytics code as well as implementing Advanced Analytics algorithms as per the solution designed by Lead data Scientist.
You should have experience in the key matrix and performance areas given below:
What we Offer in this role:
Opportunity to work in complex real world Data Science problems
Exposure to client as there will be direct client interactions
Salary: INR 14,00,000 - 24,00,000 P.A.
Industry: IT-Software / Software Services
Functional Area: Analytics & Business Intelligence
Role Category: Analytics & BI
Role: Analytics Manager
Employment Type: Permanent Job, Full Time
Desired Candidate Profile
3 6 years of experience.
3+ years in core Analytics/Advance Statistics role and having strong Machine Learning knowledge with Text Analytics and NLP
Python (mandatory) with Text Analytics libraries like NLTK, Spacy, pycorenlp, genism, etc., Machine Learning Libraries like scikit-learn, Numeric Computation libraries like TensorFLow & Theano and Deep Learning libraries like Keras
Spark MLib with Python (mandatory)
Microsoft Azure ML (Preferred)
Java (Good to have) with libraries like D4j, etc.
R (Good to have) with packages like tm, NLP, openNLP, qdap, caret, etc.
NoSQL Databases: Cassandra, MongoDB (Good to have)
Good communication and debugging skills
Experience in Text Analytics using NLP & Semantics, Text Processing (automated spell correction ,Tokenization, POS-tagging, Lemmatization, Dependency Parsing), building and using document and word vector models like Doc2vec, Word2vec and Glove for context extraction, strong Regular Expressions knowledge, Sentiment Analysis, Named Entity Recognition (both Standard and custom), Coreference Identification, Entity Relationship extraction, Ontology Building, Entity Disambiguation, Word Sense Disambiguation (WSD), Entity-Linking, Topic Modelling, Text Classification
Machine Learning techniques knowledge: Linear Regression, Logistic Regression, Tree Based ensemble models like Bagging, Boosting & Random Forests, Support Vector Machines, Nave Bayes classifiers, Maximum Entropy models (MAXENT), etc.
Deep Learning techniques knowledge like MLP, RNNs, LSTM, etc. for Text based model creation
Good communication skill (articulation using verbal & non-verbal skills, clarity of thought).
Attention to details.
Integrity & Stretch Mind-set.