OverviewThe data scientist is responsible for modeling complex business problems and discovering business insights through the use of statistical, algorithmic, mining, and visualization techniques. The data scientist contributes to building and developing the organization's data infrastructure and supports the senior leadership with insights, management reports, and analysis for decision-making processes.
Responsibilities- Understands the decision-making process, workflows, and business and information needs of business unit heads and service manager/owners.
- Translates business needs into analytics/reporting requirements to support executive decisions and workflows with required information.
- Proactively mines data warehouses to identify trends and patterns and generates insights for business units and senior leadership.
- Performs large-scale experimentation to identify hidden relationships between variables in large datasets.
- Researches and implements cutting-edge techniques and tools in machine learning/deep learning/artificial intelligence to make data analysis more efficient.
- Determines requirements that will be used to train and evolve deep learning models and algorithms.
- Visualizes information and develops engaging reports on the results of data analysis using data visualization tools.
- Advises product teams on new products, features or updates through data-based recommendations.
- Develops frameworks and processes to analyze unstructured information collected through social media platforms i.e., wikis, blogs, instant messaging, etc. and traditional sources such as e-mail and SharePoint.
- Supports user experience specialists and information architects to enhance information visualization through development of dashboards and user interfaces.
- Distributes best practices to analytics and product teams and provides consultations for their data-based experimentations.
Required qualifications- Up-to-date knowledge of machine learning and data analytics tools and techniques - GLM, Random Forest, GBM, Neural Networks, Time Series.
- Random Forest, GBM, Neural Networks, Time SeriesIntegrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing.
- Strong knowledge in predictive modeling methodology.
- Experienced at leveraging both structured and unstructured data sources.
- Willingness and ability to learn new technologies on the job.
- Demonstrated ability to communicate complex results to technical and non-technical audiences.
- Demonstrated ability to work with minimal supervision.
- Experience using statistics and machine learning to solve complex business problems.
- Experience conducting statistical analysis with advanced statistical software, scripting languages, and packages - H2O, Tensorflow, Python, SQL.
- Experience with big data analysis tools and techniques - Big Query, Hive, Spark, SQL Server.
- Testing of ML models, such as cross-validation, A/B testing, bias and fairness.
- Experience building and deploying predictive models, web scraping, and scalable data pipelines.
Preferred qualifications- Master's degree or PhD in computer science, statistics, or related fields
- 3+ years experience as a Data Scientist
- Experience in Fintech and/or Insurance industry will be a preferred
