Job description

We are looking for a candidate whose primary focus will be in applying Natural Language Processing (NLP) AI techniques, doing machine learning, and building high-quality prediction systems to classify data. Presenting information using data visualization techniques. Undertaking data collection, preprocessing, and harmonization

Roles and Responsibilities

• Develop applications in machine learning and artificial intelligence. Selecting features, building and optimizing classifiers using machine learning techniques.
• Understanding business objectives and developing models that help to achieve them, along with metrics to track their progress
• Managing available resources such as hardware, data, and personnel so that deadlines are met
• Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability
• Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world
• Verifying data quality, and/or ensuring it via data cleaning
• Supervising the data acquisition process if more data is needed
• Finding available datasets online that could be used for training
• Defining validation strategies
• Defining the pre-processing or feature engineering to be done on a given dataset
• Defining data augmentation pipelines
• Training models and tuning their hyperparameters
• Analyzing the errors of the model and designing strategies to overcome them Deploying models to production

Desired Candidate Profile

• Sound understanding of ML and DL algorithm
• Architecture level understanding of CNN RNN algorithm
• Experience with NLP data models and libraries
• Good understanding of entity extraction using NLP
• Handson tensorflow, scikit learn, spacy libraries etc
• Good knowledge of transfer learning
• Good scripting and programming skills in Python and Streamlight
• Experience with common data science toolkits, such as Python, NumPy, Transformers, Fast.AI, etc.
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Proficiency in using query languages
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Data-oriented personality. Data Wrangling and Data Exploration
• Tableau, DataPrep is a PLUS

Note – Preference for immediate joiner and salary no bar for right candidate.