Data Science Lead Analyst

Full time

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Employment Information

Job levelSenior level

Salary$141 476 - $141 476

IndustryFinancial Services

Experience4+ years

LocationNew Castle, DE, United States

Citibank, N.A. seeks a Data Science Lead Analyst for its New Castle, DE location.

Duties:

Conduct statistical analysis and optimization of financial and non-financial data. Build scalable solutions by implementing machine learning models and algorithms. Interpret and translate algorithms into programming code. Train and test models maintaining high quality code and documentation. Work cross-functionally and execute complex statistical analyses supporting fraud, conduct and cyber related investigations processes. Help in data driven decision making. Prepare data visualizations and create simulations to discover and classify patterns that help in business decision making, and present findings. Assist in upgrading visualizations for investigative analysis using Tableau. Analyze large disparate datasets. Perform statistical inference drawing conclusions from sample data. Analyze outliers in investigation log reports. Translate complex data into meaningful and digestible insights. Implement automation tools and integration of MS SQL Server, Oracle databases, Python, and R platforms. Assist in automation of data transfer within Citi SQL Server environments. Integrate HTTP data requests using Python libraries. Remote work may be permitted within a commutable distance from the worksite, in accordance with Citi policy.

Requirements:

Master’s degree, or foreign equivalent, in Data Science, Data Analytics, Business Analytics, or a related field, and two (2) years of experience in the job offered or in a related occupation. Two (2) years of experience must include:

Programming languages, including Python, SQL and R;
Big data technologies, including Hive, Hadoop, and MapReduce;
Working in Agile methodologies while using project management tools, including JIRA;
Working with software development life cycle and CI/CD processes including version control in Github, software package containerization, and scheduling engines;
Applying machine learning modeling for time series, supervised and unsupervised learning methods, deep learning using neural networks, and statistical modeling;
Dashboarding using visualization tools, including matplotlib, pyplot or seaborn in Python;
Using data visualization software, including Tableau or Power BI;
Performing natural language programming, including text extraction and analysis;

and Leading end to end data science projects and pipelining data science solutions for production. 40 hrs./wk.