Highlights
Build statistical, descriptive and predictive models using statistical techniques, such as logistic regression, clustering, principal components analysis, decision trees, correlation analysis, etc., looking to understand patterns, predict behavior, customer profiles, forecasting, among other challenges, with the purpose of minimizing the risk while increasing the bank’s revenue. The person in this role will be responsible for developing, leveraging and implementing out-of-box models of advanced statistical, descriptive, and predictive models (machine learning, supervised, unsupervised, classification, regression, and time series) for business needs.
Numbers & Facts
| Location | Miramar, FL |
Description
Responsible for business analysis using data science and business intelligence techniques. Transforms raw data into actionable insights that support strategic decision-making. Create outstanding visualization within approved visualization tools, in addition to automating reporting and conducting ad hoc analysis. The person in this role will be responsible for developing, leveraging and implementing out-of-box models of advanced statistical, descriptive, and predictive models (machine learning, supervised, unsupervised, classification, regression, and time series) for business needs. This position analyzes, proposes, and generates predictive and descriptive insights to create business opportunities. This role entails deep knowledge and business acumen in the financial industry.
This person will need to display and implement proper methodology for analysis and model cycles ensuring timely tracking, documentation, reproducibility, scalability, and insights that are actionable and used to improve future business initiatives, challenges and efforts.
Responsibilities:
Technical & Analytical Skills:
Business Intelligence
- Strong analytical and problem-solving skills with an ability to interpret complex data sets.
- Develop and maintain dashboards, reports, and visualizations using approved BI tools like Power BI, Tableau
- Analyze complex datasets to identify patterns, trends, and anomalies
- Ensure data accuracy, integrity, and security across platforms
- Familiar with Git version control and development environments
- Contribute to data quality, accuracy, and accessibility by ensuring data integrity and security.
- Identify areas for improvement and recommend solutions based on data analysis
Analytics and Predictive Modeling
- Build statistical, descriptive and predictive models using statistical techniques, such as logistic regression, clustering, principal components analysis, decision trees, correlation analysis, etc., looking to understand patterns, predict behavior, customer profiles, forecasting, among other challenges, with the purpose of minimizing the risk while increasing the bank’s revenue.
- Stay abreast of emerging machine learning and data science technologies.
- Identify and uncover business opportunities from data mining and analytical studies and tasks.
- Support the automation of the information requirements from the lines of business.
- Responsible for supporting ad-hoc and special project requirements of the area’s Manager.
Communication Skills:
- Excellent written and verbal communication skills to present findings effectively in a compelling manner.
Project Management and Leadership:
- Proven ability to manage projects independently, prioritize effectively in a fast-paced environment, and find creative solutions.
- Leverage on personal relations to expedite and influence outcomes.
- Attain scheduled project completion deadlines.
Collaboration and Teamwork:
- Collaborate effectively with cross-functional teams and internal stakeholders to identify business needs and address challenges to define business requirements and translate them into data solutions.
- Team player that can take feedback to constantly do better.
- Collaborate with the Model Risk Management (MRM) Unit with the validation of the models, providing the required documentation, executing back testing, and on-going model monitoring.
- Perform special projects as assigned.
- Work with cross-functional teams, including data scientists, engineers, and business users, to ensure data-driven decision-making
- Ability to work collaboratively in a team environment and manage multiple projects simultaneously.
Business Acumen and Strategic Thinking:
- Translates data and analysis into actionable business strategies.
- Shares insights with business partners and executives to inform decision-making.
Knowledge Sharing and Mentorship:
- Provides expert advice and guidance to stakeholders and team members.
- Identify and uncover business opportunities from data mining and analytical studies and tasks.
Other:
- Ensure preventive measures are carried out to fully comply with current rules, regulations and internal policies relating to risks pertaining to BSA, USA Patriot Act, OFAC and other AML related issues
- Perform other duties as assigned.
Minimum Education and/or Certifications Requirements:
- A bachelor’s or master’s degree in statistics, computer science, data science, operations research, applied mathematics, engineering, artificial intelligence, machine learning, predictive analytics, or a related quantitative field.
- A certification in open-source coding, as R or Python
- A SAS certification is a plus
Minimum Work Experience Requirements:
- At least 4 to 5 years of experience in developing and implementing advanced predictive models, data mining or data science projects and Business Intelligence.
- +4 years of experience working on complex projects and in the financial industry.
Technical and/or Other Essential Knowledge:
- Substantial coding knowledge in at least one of the following languages: SAS, R, and Python is requested
- Knowledge in statistical and data mining techniques: generalized linear model (GLM)/regression, random forest, boosting, trees, text mining, hierarchical clustering, deep learning, Neural Network (NN), graph analysis, etc.
- Knowledge of Data Mining tools, such as SAS Enterprise Miner is desirable
- Knowledge in popular database programming languages, such as MS SQL, relational databases and upcoming nonrelational databases such as NoSQL databases is a plus
- Substantial knowledge in data visualization tools, such as Power Bi