Responsibilities:

1.      Work with stakeholders across the organization to identify opportunities to leverage enterprise data to drive business solutions.

2.      To mine and analyze data from corporate databases to drive optimization and improvement of product development, marketing methods, and business strategies.

3.      Evaluate the effectiveness and accuracy of new data sources and data collection techniques.

4.      Develop custom data models and algorithms to apply to your datasets.

5.      Use predictive modeling to improve and optimize customer experience, revenue generation, ad targeting, and other business outcomes.

6.      Develop your company's A/B testing framework and test model quality. Coordinate with various functional teams to implement models and monitor results.

7.      Develop processes and tools for monitoring and analyzing model performance and data accuracy.

Required Skillset:

1.   Data mining through state-of-the-art methods.
2.   Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
3.   Experience working with ML/DL frameworks like Scikit-Learn, TensorFlow, Keras, PyTorch, etc. as well as a firm grip over modern state-of-the-art and otherwise NLP architectures such as BERT, GPT3, Reformer, RNNs, CNNs, LSTMs, etc. and vector space models such as GloVe, Word2vec, USE, etc.
4.   Good to have Familiarity with Model Serving APIs like, Django, Flask & StreamLit. Peer-reviewed publications in topics of advanced data science, machine learning, deep learning, or NLP.
5.   Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests, proper usage, etc.) and experience with applications.
6.   Expert knowledge and Experience in working on conventional Computational Linguistics problems such as Syntactic Processing, Word Sense Disambiguation, Context-Free grammar, Lexical Semantics, Quantification, Plurality, etc.
7.   Expert knowledge and Experience in working on conventional NLP problems like Informational Retrieval, Relevance Ranking, and Search, Question-Answer Generation, Natural Language Inference, Topic Modeling, Unstructured Text Pipelines/Modeling, Anomaly Detection, Text Summarization, etc.
8.   Experience using statistical computer languages (Python, SQL, etc.) to manipulate data and draw insights from large data sets.
9.   A drive to learn and master new technologies and techniques. Certifications in the relevant domain and demonstratable experience in research and development are a plus.
10.   Good to have peer-reviewed publications on topics of advanced data science, machine learning, deep learning, or NLP.

Qualification & Experience:

Bachelor’s degree in Computer Engineering, Computer Science, Data Science, or relevant subject. And the experience of working 3+ years in Data science or similar domains. MS/Ph.D. or particular international certification in a relevant field would be a plus.