The role of Data science, Lead provides an opportunity to be a part of the Near Data Science team. You’ll join a team of experts in data science applied to location-based intelligence. They carry out R&D, prototyping, development and deployment, of data science solutions applied to the world of Digital Marketing and Ad Tech. The role requires you to apply your extensive knowledge to designing, development, deployment and defining best practices for the Data Science team and to partner with key decision-makers in business, product and engineering teams within the company.
As a Data Science-Lead, you will collaborate with your team members, Software Engineers, Data Engineers and Data Analysts to develop data-driven products. You should have the ability to envision the Near Products and their feature enhancements; you will lead the team to solve difficult challenges and set the pace. This role is hands-on and you will be developing models and pipelines along with the rest of the team, while you will mentor the rest of the team with the latest technologies and trends in data science.
As part of the data science team at Near, one of the fastest-growing Enterprise SaaS companies, you will be part of a true start-up culture, where you are given the freedom to experiment and innovate new winning ways – a great opportunity for people who can work independently and are self-driven.
- Developing core data science models and capabilities that power the Near Platform and its SaaS products.
- Applying various data science methods such as time series forecasting, causal inference, machine learning methods and reinforcement learning to understand the most important aspects of our product, users, and business.
- 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.
- Project management of data science projects to ensure they are delivered on time.
- 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.
- Partner with technology and the business teams to build a superior data quality pipeline that will feed the models.
- 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.
Skills and Requirements
- Hold an advanced degree in M.Tech/PhD in a quantitative field (e.g. Computer Science, Econometrics, STEM fields) plus.
- Overall 8-12 years of experience with at least a minimum 5 years working experience on any data-driven company/platform, developing data science models and quantitative models.
- Ability to work independently with high energy, enthusiasm and persistence.
- Must have exposure in handling multiple simultaneous projects and meet deadlines and can work in a group setting as well as in an independent position.
- Must have thorough mathematical knowledge of correlation/causation, decision trees, classification and regression models, recommenders, probability and stochastic processes, distributions, priors and posteriors.
- 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, Combinatorial optimization is a plus.
- Familiarity with Python, Apache Spark (Java, Scala, Python), ANSI SQL, AWS Cloud, 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.
- The candidate is expected to have exceptional problem solving, analytical and organization skills with a detail-oriented attitude.