Job title: Data Scientist
Job description: Job Description ROLE PURPOSE: Data Scientists extract value from data using advanced analytics techniques. This role is responsible for experimenting with data, identifying patterns, applying statistical analysis, build data science models to Job Description ROLE PURPOSE: Data Scientists extract value from data using advanced analytics techniques. This role is responsible for experimenting with data, identifying patterns, applying statistical analysis, build data science models to predict and also engage in development of Artificial Intelligence / Machine Learning based solutions. Data scientist will determine requirements to train and evolve deep learning models and algorithms. Using all these techniques and technologies, data scientists will help make decisions and predict future outcomes of certain products and services. The role will help data & analytics team in the planning, evaluating, and selecting the right data science capabilities for the Bank. The role serves as an advisor to the data management team to develop and deliver enterprise-wide solutions for complex data analytic challenges in Fraud Risk Management. Roles and Responsibilties: 1. Use advanced analytics methods to extract value from business data 2. Manage the fraud prevention and detection rules in all fraud tools including but not limited to Fraud Guard, EFMS, SAS VI, SAS VA, VRM & FRM. 3. Analyze fraud trends on a periodic basis and provide recommendation to FRM management to implement new fraud prevention & detection controls. 4. Create periodic dashboards and presentations for senior management 5. Perform large-scale experimentation and build data-driven models to answer business questions 6. Conduct research on cutting-edge techniques and tools in machine learning/deep learning/artificial intelligence 7. Determine requirements that will be used to train and evolve deep learning models and algorithms 8. Articulate a vision and roadmap for the exploitation of data as a valued corporate asset 9. Influence FRM teams through presentation of data-based recommendations 10. Evangelize best practices to analytics and FRM teams 11. Assemble large, complex data sets that meet functional / non-functional business requirements. 12. Identify, design, and implement internal process improvements: automating manual processes, optimizing analytics delivery, re-designing infrastructure for greater scalability, etc. 13. Build analytics tools that utilize the data to provide actionable insights into customer acquisition, operational efficiency and other key business performance metrics. 14. Work with stakeholders including the FRM and FMU teams to assist with analytics related technical issues and support their data infrastructure needs. 15. Work with data and analytics experts to strive for greater functionality data systems. 16. Ensure that all data science related work meets data security requirements. 17. Ensure all process of the analytics section of FRM is documented and maintained as an SOP. 18. Maintains relevant documentation to enable peers and other teams to benefit from data science use cases implemented. Requirements Specialist Skills / Technical Knowledge, Technical Competencies Required for this role Strong problem-solving skills with an emphasis on product development. Experience using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and drawinsights from large data sets. Can work with data to identify patterns. Use judgement to form conclusions that may challenge conventional wisdom and focus on the crux of issues to identify high-leverage intervention points and strategies. Rapidly acquire new knowledge and learn new skills Seek to understand business needs and get results that have a clear, positive, and direct impact on business performance Apply different strategies to convince others to change their opinions or plans and ensure that proposals or arguments are supported by strong logic and a compelling business case, addressing all relevant factors. Consider the relative costs and benefits of potential actions to choose the most appropriate one Communication and storytelling Teamwork and collaboration Banking domain business knowledge Advanced analytics modelling and orchestration Solid development skills in Java, Scala and SQL Expertise in Advanced Analytics, Data Mining , Credit Risk Modeling and Predictive Modeling. Expertise in Machine Learning techniques for Regression: Random Forest/Linear/Multiple Regression, Classification techniques: Random Forest/SVC/KNN/Logistic, etc., Clustering using Python and SAS. Knowledge of Natural Language Processing(NLP),Text Mining and Sentiment Analysis ,created NLP models Experienced in Statistical Analysis: Descriptive, Inferential Analytics, Predictive Analytics , etc. Sound knowledge of using data science tools and languages like Cloudera Data Science Workbench (CDSW), Jupyter Notebook, Python etc. Clear hands-on mastery in big datab systems – Hadoop ecosystem, Cloud technologies (AWS, Azure, Google), in-memory database systems (HANA, Hazel cast, etc) and other database systems – traditional RDBMS (Terradata, SQL Server, Oracle), and NoSQL databases (Cassandra, MongoDB, DynamoDB) Comfortable in dashboard development (Tableau, Powerbi, Qlik, etc) and in developing data analytics models (R, Python, Spark) Hypothesis Testing- Test of Independence, ANOVA, Chi-Square, Test for Goodness of fit, etc. Model Building, Model Validation, Model Selection, Model Evaluation & Optimization and Monitoring . Previous Experience: At least 6+ years of progressively responsible relevant experience in data engineering, including creating partnerships, implementing data science solutions, and understanding the underlying technologies needed to enable data science across a midd or large organization Extensive experience working with Big Data tools and building data solutions for advanced analytics Experience with statistical software, scripting languages, and packages (e.g. R, MATLAB, SAS, BO and Python) Considerable experience in solving business problems with advanced analytical solutions Proven experience in conducting statistical analysis and building models with advanced scripting language such as R, SPSS, or other analytic tools. Experience building and deploying predictive models, web scraping, and scalable data pipelines. Strong understanding of statistical methods and skills such as Bayesian Networks Inference, linear and non-linear regression, hierarchical, mixed models/multi-level modeling Familiar with big data technologies (e.g. Cloudera), Data Engineering (e.g. Informatica), Data Streaming (Kafka), CDC, etc.
Location: Abu Dhabi
Job date: Sat, 23 Oct 2021 22:11:56 GMT
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