Data Science is at the heart of Snapp’s products and decision-making. As a member of the Data Science team, you will work in a dynamic environment, where we embrace moving quickly to build the world's best transportation and many other services. Data Scientists take on a variety of problems ranging from shaping critical business decisions to building algorithms that power our internal and external products. We work closely with many teams of the company to improve their products and decisions.
As a Data Scientist, Decisions, you will leverage data and rigorous, analytical thinking to shape the company's product and business decisions. You will identify and scope opportunities, shape priorities, recommend solutions, design experiments, and measure impact. You will bring a quantitative mindset to decision-making in partnership with product, business, and operations stakeholders throughout the organization.
- Create and develop predictive models using AI/ML technologies, installing/monitoring the production performance of models
- Partner with business and other teams to understand business and integrate additional data required for creating model.
- Data feature engineering to collect and structure to enhance modeling/analytical framework.
- Work closely with engineers to deploy models in production both in real time and in batch process and systematically track model performance.
- Be a subject matter expert on machine learning and predictive modeling.
- Drive technical innovation through active research and applications of new theories, techniques and technologies
- Minimum Experience of 2 years as role of Data Scientist or similar
- Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
- Knowledge and experience of the Python Libraries: Pandas, NumPy, SciPy, StatsModels, scikit learn, Seaborn, and Matplotlib.
- Strong understanding of machine learning techniques and algorithms.
- Knowledge and experience of the deep learning and deep learning libraries such as tensorflow, keras, and pytorch.
- Knowledge and experience in image processing and NLP is a plus.
- Advanced SQL knowledge and experience working with both relational and non-relational databases, query authoring as well as working familiarity with a variety of other databases and datasets.
- Understanding the model build life-cycle, including feature selection and optimization, model selection and validation, and ongoing model maintenance.