ML Lead

ML Lead

Location: Bangalore

Technical Specification

If you are looking to work in a fast paced product development company where your skills could flourish and if you have done lot of good python programming in cloud environment, then we have a brilliant opportunity for you. At Entomo, not only will you prosper professionally but you will see the direct impact of your work towards engaged customers. Purpose We are looking for a Machine Learning expert to lead our efforts in creating Analytics and Natural Language Processing (NLP) based products. Your responsibilities include creating machine learning models and retraining systems. To do this job successfully, you need exceptional skills in statistics and programming. If you also have knowledge of data science and software engineering, we’d like to meet you. Your ultimate goal will be to shape and build efficient self-learning product. Skills and Qualifications

• 2 years’ experience as a Sr ML/NLP Engineer or similar role

• Hands-on in machine learning frameworks (like TF+Keras or PyTorch or Spark+h2o) and libraries (like scikit-learn and pandas)

• Expertise in OOAD Python

• Expertise in visualizing and manipulating big datasets

• Deep understanding of text representation techniques (such as n-grams, bag of words, sentiment analysis, Large Language models), statistics and classification algorithms

• Proficiency with OpenCV

• Degree in Computer Science, Mathematics, Computational Linguistics or similar field

Responsibilities

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

• Analysing the ML/NLP 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

• 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 hyper parameters

• Analysing the errors of the model and designing strategies to overcome them

• Deploying models to production