[961] Data Scientist

Job Details

• 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.