Our success in making our cities congestion & pollution free is highly dependent on how closely we study & how accurately we predict our customers, their preferences, market, demand, competition, pricing, traffic, security, you name it, literally! And our data science team does this. This role will offer you an opportunity to

We are looking for an experienced Senior Data Scientist to join our talented engineering team. As our data guru, you- ll be responsible for analyzing our large data set and making recommendations that will impact major business decisions. We are looking for a proven technical leader that can excel in a fun, fast-moving startup environment and help us elevate the customer experience.

Requirements

- 3-5+ years of experience in Data Science/Machine Learning

- Familiarity with a broad set of modeling techniques; aptitude in mathematics, probability, algorithms

- Familiarity with experimentation methods, hypothesis testing

- Ability to collaborate with teams across the organization regarding business problem formulation, solution architecture, production integration

- Ability to mentor junior team members

- Ability to communicate with peer and leadership teams on project scope, expectations, progress and roadmap

- Knowledge of data query and ETL operations in SQL/Pig/Hive

- Hands-on programming/implementation ability in R/Python/Java

Job Summary & Responsibilities

- Manage the continuous improvement of data science and analytics by researching industry best practices and staying up-to-date on analytical practices

- Integrate data science solutions into current business processes

- Rapidly prototype early-stage solutions and design / evaluate predictive models and advanced algorithms to drive business decisions throughout the customer lifecycle (prospecting, acquisition, underwriting, fraud, collections, enhancing customer experience)

- Understand the systems and the business processes that populate those systems with data

- Carry out data processing including statistical analysis, variable selection, and dimensionality reduction, custom attribute engineering, as well as the evaluation of new data sources

- Leverage methods from diverse disciplines such as traditional modeling, machine learning, deep learning, artificial intelligence, statistical modeling, information theory, information retrieval, and other areas to gain customer insights, draw conclusions and work with business partners to put those insights into action

- Participate in data architecture decisions and partner with technology teams to implement models/algorithms in production

- Help document your assumptions and methodologies, as well as carry out validation and testing to facilitate peer reviews and independent model validation

- Think strategically on a higher level, proposing new business metrics or suggesting alternatives, creating highly interpretive models that imply new context and new semantics for data

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