Primarily be a self-managing individual contributor. Research and develop new cutting-edge AI algorithms using techniques such as supervised/unsupervised ML, statistical/Bayesian inference etc. to power the AI & Analytics Engine with better, smarter, and more diverse recommendations for the end users;
Bring your novel ideas to a production-ready state by implementing them as modules, while adhering to industry standards in coding: quality, maintainability, and in conformance with the overall architecture are of paramount importance;
Participate in product-steering sessions in your technical capacity to:
Pitch new ideas to improve the product;
Respond to user feedback and business requirements by providing the necessary technical directions from a Data Scientist’s perspective, to put items on the product roadmap;
Participate in the delivery of our product to customers and other customer facing activities;
Participate in technical content writing. This may be in the form of product documentation, blog articles highlighting new features and use cases, as and when the need arises;
Understand and advocate our long-term vision while working with the management and product teams to define and adapt the same; and
Contribute to the evangelisation of our product and our culture internally and externally.
JOB REQUIREMENTS
A self-starter with a strong passion for excellence. You always want to go above and beyond in everything you do;
An entrepreneurial “can-do” attitude and an innovative mindset;
Savvy and professional, ethical, and well developed interpersonal skills;
Preferably holding a Masters by Research, PhD or Postdoc in computer science (includes AI and Data Science), mathematics, physics, or engineering;
Sound in the mathematical foundations of ML/AI coupled with practical skills in these fields;
Good hands-on skills in statistical modeling and data analysis (pandas, Dask, Spark, etc.);
Experience with multiple ML/DL frameworks (scikit-learn, Dask, Spark Mllib/ML, XGBoost, LightGBM, TensorFlow, Torch, etc.). We expect you to know when and how best to employ them and understand various trade-offs.;
Understanding of performance issues such as scalability, complexity, and memory footprint of various algorithms;
Sound problem solving skills and algorithmic approach;
Expert understanding in at least one specialisation area within ML/AI:
Modern NLP/NLU using DL frameworks;
Scaling supervised and unsupervised ML algorithms to large data;
Bayesian Optimisation (for hyperparameter search and model selection);
PU Learning;
Active Learning;
Time-series analysis including classification, forecasting, and survival analysis;
GPU acceleration for ML/DL;
Explainable AI;
Applications of graphical models in AI such as Bayesian networks, Markov random fields, etc.
Demonstrated ability to write code that can run in production, in at least one of the following languages: Python, R, Scala, Julia, Java, C++, or Go.