You will be joining Near, one of the fastest-growing Enterprise SaaS companies, and experience a true start-up culture with the freedom to experiment and innovate. At Near, we believe that great culture is not just about work; it’s work + life. We not only encourage our employees to dream big but also give them the freedom and the tools to do so.
This role provides an opportunity to be a part of the Near Data Science team. You will have the chance to get your hands dirty working on a cutting-edge tech stack and leverage your skills and toolset to help us build an awesome product.
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.