Lead - Talent Acquisition at Near
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Near - Senior Data Science (4-8 yrs)
In the role of Senior Data Science in the Near's Data Science team, you'll join a team of experts in data science applied to location-based intelligence. This team carries 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, developing, 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 Senior Data Scientist, 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 Near's products and their feature enhancements. This role requires you to be hands-on and you will be developing models and pipelines along with the rest of the team.
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.
- Researching and creating 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 and product team to operationalize and deploy the models.
- Partnering with technology and the business teams to build a superior data quality pipeline that will feed the models.
- Understanding and prioritizing data science work based on cost-effectiveness and leveraging time management skills.
- Attending conferences and organizing workshops/meet-ups to be in touch with the data science community.
Skills and Requirements
- An advanced degree in M.Tech/PhD in a quantitative field (e.g. Computer Science, Econometrics, STEM fields) plus.
- Overall 4-8 years of experience with at least 3 years work 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 meeting deadlines. Should be able to work in a group setting as well as in an independent position.
- Must have good mathematical knowledge of probability and stochastic processes distributions, priors and posteriors.
- Writing complex transformation logic to generate independent and dependent variables, feature selection, model selection and tuning, performance measures and generating production-ready code.
- Should have worked on Correlation/causation, decision trees, classification and regression models, recommender systems, deep neural networks and NLP.
- Familiarity with Python programming, Apache Spark (Java, Scala, Python), ANSI SQL, AWS Cloud, PyMC3 /theano /tensorflow and other scientific python/R modules is a plus.
- Should have worked on the deployment of models, ML-ops, well versed with Github and best coding practices CI/CD.
- 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.
Near is the world's largest source of intelligence on people and places, processing data from over 1.6 billion monthly users across 44 countries. The Near Platform powers data-driven marketing and enrichment offerings through a suite of SaaS products. The users of the platform can leverage audience, spatial, retail, among other data in a privacy-led environment.
Founded in 2012, Near is headquartered in Singapore with offices in San Francisco, New York, London, Bangalore, Tokyo, and Sydney. Today, marquee brands such as News Corp and Mastercard work with Near to provide enhanced customer experiences.
Near is backed by leading investors including Sequoia Capital, JP Morgan Private Equity Group, Cisco Investments, Telstra Ventures, and Greater Pacific Capital. Visit www.near.co to find out more.