Lead Data Scientist

8 - 10 years of total relevant experience with hands-on programming skills in Python,

R, etc.

 Experience on minimum End to End production rollouts / implementations of Data

Science and analytics projects

 Minimum of 4 years in technical execution roles in Data Science, Data Analytics &

Machine Learning projects.

 Experience on Deep learning techniques (Sentiment Analysis, Text Mining, Entity

Extraction, Document Classification, Topic Modelling, Natural Language

Understanding (NLU) and Natural Language Generation (NLG))

 Experience in Web Scraping or data extraction through Selenium / Scrapy or other

frameworks and related libraries. Knowledge of advanced scraping techniques (like

overcoming captcha, proxy, browser fingerprinting, bot detection bypassing, etc)

 Experience on Working knowledge in various DBs (MySQL, HBase, MongoDB),

message queues & Web Restful APIs.

 Expertise in open-source NLP toolkits such as CoreNLP, OpenNLP, NLTK, gensim,

LingPipe, Keras, Tensorflow, Mallet, ML/math toolkits such as scikit-learn, MLlib,

Theano, NumPy, etc.

 Experience of development, testing & deployment of Machine learning/deep learning

projects in the on premise /cloud platforms.

 Experience in diverse Data Science modelling including deep learning and reinforced

learning.

 Experience on ETL tools like Talend, Data stage

 Experience in Big Data and related technologies: Map/Reduce, Hadoop, Hive, Spark,

SQL, Mongo DB, HDFS etc.

 Experience in Analytics - Reports and Dashboards

Industry Preferably from a Technology, ITES, Products, Professional SI organization with a focus on

BFSI.

Responsibilities

 He/she will be a technical hands on / delivery role responsible for executing

challenging and complex data science and engineering projects

 Technically lead a team of data scientists and engineers for expert technical vision,

analytical and business intelligence

 Responsible for data science professional design, development and deployment

in the use of modern data science tools.

 Performs deep analyses, builds models/ algorithms and evaluate the models to

understand business objectives

 Analyse large amount of raw information to find patterns, build data products and

models to extract valuable business insights.

 Build, develop, coach and lead a team of data professionals in the area of Data

Engineering, Business Intelligence, Analytics, Data Science, Experimentation, AI & ML.

 Perform statistical and exploratory data analysis

 Research and build appropriate Ai/ML algorithms and review the (code)

implementation of team members

 Identify valuable data sources and architect/automate ETL strategies

 Develop and maintain optimal data pipeline architecture

 Analyse large amounts of information to discover trends and patterns

Certifications Data Science, Data Analytics, Big Data Certifications are preferred,