A Day in the Life

• Developing ‘core’ data science models and capabilities – that power Near‘s Location Intelligence Platform and associated products.
• Advanced data analytics include processing structured (payments, telecom, page clicks, etc) and unstructured data in multiple formats (text, audio, video) spanning multiple domains including user profile data, geo-spatial data, network data, and retail data.
• Primary responsibility will be to train, test, and validate models for analytics and production and generate reports at a fixed cadence.
• Research and create intellectual property for the company that will benefit Near and its partners.
• Use nonparametric and probabilistic models to generate insights keeping in mind the bias-variance trade-off.
• Working closely with the Engineering team to ‘operationalize’ and deploy the models.
• Mentor/share knowledge of data science with other global members of the Near, document, and partner with others as a team to deliver the maximum value for the company.
• Understand and prioritize the data science work based on cost-effectiveness and leveraging time management skills.
• Attend conferences and organize workshops/meet-ups to be in touch with the data science community.

What You Bring to the Role

• Bachelor’s/Master’s degree in B.Tech/M.Tech, Ph.D. is preferred.
• Overall 6-9 years of experience with at least a minimum of 3 years working experience on any data-driven company/platform, industry experience in developing data science models, and must have published a few research papers.
• Must have completed academic projects in data science experimenting with raw data and generating insights, publications are a plus.
• Must have thorough mathematical knowledge of correlation/causation, decision trees, classification, and regression models, recommenders, probability, and stochastic processes, distributions, priors, and posteriors.
• Skilled in scientific programming languages such as Python, Java, R, Matlab, Clojure, and writing deployable code into production.
• Understand the model lifecycle of cleansing/standardizing raw data, feature creation/selection, writing complex transformation logic to generate independent and dependent variables, model selection, tuning, A/B testing, and generating production-ready code.
• Knowledge of Numerical optimization, Linear/Non-linear/Integer programming, Statistics, and Combinatorial optimization is a plus.
• Familiarity with R, Apache Spark (Java, Scala, Python), PyMC3/theano/TensorFlow, and other scientific python/R modules is a plus.
• Need to be comfortable writing code for model building and bootstrap, test and own models through their lifecycle including DevOps and deploying into the cloud.
• Passion for learning new technologies and being up-to-date with the scientific research community.